diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml index 73f524d9..2b430902 100644 --- a/dev/pkgdown.yml +++ b/dev/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.11 pkgdown: 2.1.1 pkgdown_sha: ~ articles: {} -last_built: 2024-11-22T09:57Z +last_built: 2024-11-27T12:37Z urls: reference: https://mlr3mbo.mlr-org.com/reference article: https://mlr3mbo.mlr-org.com/articles diff --git a/dev/reference/mlr_loop_functions_parego.html b/dev/reference/mlr_loop_functions_parego.html index 1132704b..873ff8e2 100644 --- a/dev/reference/mlr_loop_functions_parego.html +++ b/dev/reference/mlr_loop_functions_parego.html @@ -203,8 +203,8 @@

Examples optimizer$optimize(instance) } -#> WARN [09:58:15.435] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this -#> WARN [09:58:15.460] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. +#> WARN [12:37:58.964] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this +#> WARN [12:37:58.990] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. #> x x_domain y1 y2 #> <num> <list> <num> <num> #> 1: 0.7590663 <list[1]> 0.5761817 1.539916 diff --git a/dev/reference/mlr_optimizers_mbo.html b/dev/reference/mlr_optimizers_mbo.html index 07ce5545..0dfc15b9 100644 --- a/dev/reference/mlr_optimizers_mbo.html +++ b/dev/reference/mlr_optimizers_mbo.html @@ -356,8 +356,8 @@

Examples optimizer$optimize(instance) } -#> WARN [09:58:18.312] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this -#> WARN [09:58:18.312] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. +#> WARN [12:38:01.985] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this +#> WARN [12:38:01.986] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. #> x x_domain y1 y2 #> <num> <list> <num> <num> #> 1: 1.5279770 <list[1]> 2.3347137 0.2228057 diff --git a/dev/reference/mlr_tuners_mbo.html b/dev/reference/mlr_tuners_mbo.html index 41b9a8d7..e0ddeee0 100644 --- a/dev/reference/mlr_tuners_mbo.html +++ b/dev/reference/mlr_tuners_mbo.html @@ -265,13 +265,7 @@

Examples tnr("mbo")$optimize(instance) } -#> cp learner_param_vals x_domain classif.acc selected_features -#> <num> <list> <list> <num> <num> -#> 1: -3.599781 <list[2]> <list[1]> 0.8594162 2.666667 -#> 2: -8.204952 <list[2]> <list[1]> 0.8594162 2.666667 -#> 3: -1.297196 <list[2]> <list[1]> 0.8142185 2.000000 -#> 4: -5.902366 <list[2]> <list[1]> 0.8594162 2.666667 -#> 5: -1.305511 <list[2]> <list[1]> 0.8142185 2.000000 +#> Error: Measure 'selected_features' requires the trained model # } diff --git a/dev/search.json b/dev/search.json index aa986529..665d3d5d 100644 --- a/dev/search.json +++ b/dev/search.json @@ -1 +1 @@ -[{"path":"https://mlr3mbo.mlr-org.com/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Lennart Schneider. Maintainer, author. Jakob Richter. Author. Marc Becker. Author. Michel Lang. Author. Bernd Bischl. Author. Florian Pfisterer. Author. Martin Binder. Author. Sebastian Fischer. Author. Michael H. Buselli. Copyright holder. Wessel Dankers. Copyright holder. Carlos Fonseca. Copyright holder. Manuel Lopez-Ibanez. Copyright holder. Luis Paquete. Copyright holder.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Schneider L, Richter J, Becker M, Lang M, Bischl B, Pfisterer F, Binder M, Fischer S (2024). mlr3mbo: Flexible Bayesian Optimization. R package version 0.2.8.9000, https://github.com/mlr-org/mlr3mbo, https://mlr3mbo.mlr-org.com.","code":"@Manual{, title = {mlr3mbo: Flexible Bayesian Optimization}, author = {Lennart Schneider and Jakob Richter and Marc Becker and Michel Lang and Bernd Bischl and Florian Pfisterer and Martin Binder and Sebastian Fischer}, year = {2024}, note = {R package version 0.2.8.9000, https://github.com/mlr-org/mlr3mbo}, url = {https://mlr3mbo.mlr-org.com}, }"},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"mlr3mbo","dir":"","previous_headings":"","what":"Flexible Bayesian Optimization","title":"Flexible Bayesian Optimization","text":"Package website: release | dev new R6 much modular implementation single- multi-objective Bayesian Optimization.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get Started","title":"Flexible Bayesian Optimization","text":"best entry point get familiar mlr3mbo provided via Bayesian Optimization chapter mlr3book.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"design","dir":"","previous_headings":"","what":"Design","title":"Flexible Bayesian Optimization","text":"mlr3mbo built modular relying following R6 classes: Surrogate: Surrogate Model AcqFunction: Acquisition Function AcqOptimizer: Acquisition Function Optimizer Based , Bayesian Optimization (BO) loops can written, see, e.g., bayesopt_ego sequential single-objective BO. mlr3mbo also provides OptimizerMbo class behaving like Optimizer bbotk package well TunerMbo class behaving like Tuner mlr3tuning package. mlr3mbo uses sensible defaults Surrogate, AcqFunction, AcqOptimizer, even loop_function. See ?mbo_defaults details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"simple-optimization-example","dir":"","previous_headings":"","what":"Simple Optimization Example","title":"Flexible Bayesian Optimization","text":"Minimize two-dimensional Branin function via sequential BO using GP surrogate EI acquisition function optimized via local serch: can quickly visualize contours objective function (log scale) well sampling behavior BO run (lighter blue colours indicating points evaluated later stages optimization process; first batch given initial design). Note can also use bb_optimize shorthand instead constructing optimization instance.","code":"library(bbotk) library(mlr3mbo) library(mlr3learners) set.seed(1) fun = function(xdt) { y = branin(xdt[[\"x1\"]], xdt[[\"x2\"]]) data.table(y = y) } domain = ps( x1 = p_dbl(-5, 10), x2 = p_dbl(0, 15) ) codomain = ps( y = p_dbl(tags = \"minimize\") ) objective = ObjectiveRFunDt$new( fun = fun, domain = domain, codomain = codomain ) instance = oi( objective = objective, terminator = trm(\"evals\", n_evals = 25) ) surrogate = srlrn(lrn(\"regr.km\", control = list(trace = FALSE))) acq_function = acqf(\"ei\") acq_optimizer = acqo( opt(\"local_search\", n_initial_points = 10, initial_random_sample_size = 1000, neighbors_per_point = 10), terminator = trm(\"evals\", n_evals = 2000) ) optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer ) optimizer$optimize(instance) ## x1 x2 x_domain y ## ## 1: 3.104516 2.396279 0.412985 library(ggplot2) grid = generate_design_grid(instance$search_space, resolution = 1000L)$data grid[, y := branin(x1 = x1, x2 = x2)] ggplot(aes(x = x1, y = x2, z = log(y)), data = grid) + geom_contour(colour = \"black\") + geom_point(aes(x = x1, y = x2, colour = batch_nr), data = instance$archive$data) + labs(x = expression(x[1]), y = expression(x[2])) + theme_minimal() + theme(legend.position = \"bottom\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"simple-tuning-example","dir":"","previous_headings":"","what":"Simple Tuning Example","title":"Flexible Bayesian Optimization","text":"","code":"library(mlr3) library(mlr3learners) library(mlr3tuning) library(mlr3mbo) set.seed(1) task = tsk(\"pima\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-04, upper = 1, logscale = TRUE)) instance = tune( tuner = tnr(\"mbo\"), task = task, learner = learner, resampling = rsmp(\"holdout\"), measure = msr(\"classif.ce\"), term_evals = 10) instance$result ## cp learner_param_vals x_domain classif.ce ## ## 1: -6.188733 0.2382812"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Base Class — AcqFunction","title":"Acquisition Function Base Class — AcqFunction","text":"Abstract acquisition function class. Based predictions Surrogate, acquisition function encodes preference evaluate new point.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Acquisition Function Base Class — AcqFunction","text":"bbotk::Objective -> AcqFunction","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Base Class — AcqFunction","text":"direction (\"\" | \"minimize\" | \"maximize\") Optimization direction acquisition function relative direction objective function bbotk::OptimInstance related passed bbotk::Archive. Must \"\", \"minimize\", \"maximize\". surrogate_max_to_min (-1 | 1) Multiplicative factor correct minimization maximization acquisition function. label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object. archive (bbotk::Archive) Points bbotk::Archive surrogate. fun (function) Points private acquisition function implemented subclasses. surrogate (Surrogate) Surrogate. requires_predict_type_se (logical(1)) Whether acquisition function requires surrogate \"se\" $predict_type. packages (character()) Set required packages.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Base Class — AcqFunction","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Base Class — AcqFunction","text":"AcqFunction$new() AcqFunction$update() AcqFunction$reset() AcqFunction$eval_many() AcqFunction$eval_dt() AcqFunction$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Base Class — AcqFunction","text":"Creates new instance R6 class. Note surrogate can initialized lazy can later set via active binding $surrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$new( id, constants = ParamSet$new(), surrogate, requires_predict_type_se, direction, packages = NULL, label = NA_character_, man = NA_character_ )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"id (character(1)). constants (paradox::ParamSet). Changeable constants parameters. surrogate (NULL | Surrogate). Surrogate whose predictions used acquisition function. requires_predict_type_se (logical(1)) Whether acquisition function requires surrogate \"se\" $predict_type. direction (\"\" | \"minimize\" | \"maximize\"). Optimization direction acquisition function relative direction objective function bbotk::OptimInstance. Must \"\", \"minimize\", \"maximize\". packages (character()) Set required packages. warning signaled prior construction least one packages installed, loaded (attached) later -demand via requireNamespace(). label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Base Class — AcqFunction","text":"Update acquisition function. Can implemented subclasses.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Base Class — AcqFunction","text":"Reset acquisition function. Can implemented subclasses.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-eval-many-","dir":"Reference","previous_headings":"","what":"Method eval_many()","title":"Acquisition Function Base Class — AcqFunction","text":"Evaluates multiple input values acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$eval_many(xss)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"xss (list()) list lists contains multiple x values, e.g. list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Base Class — AcqFunction","text":"data.table::data.table() contains one y-column single-objective acquisition functions multiple y-columns multi-objective acquisition functions, e.g. data.table(y = 1:2) data.table(y1 = 1:2, y2 = 3:4).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-eval-dt-","dir":"Reference","previous_headings":"","what":"Method eval_dt()","title":"Acquisition Function Base Class — AcqFunction","text":"Evaluates multiple input values objective function","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$eval_dt(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"xdt (data.table::data.table()) One point per row, e.g. data.table(x1 = c(1, 3), x2 = c(2, 4)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Base Class — AcqFunction","text":"data.table::data.table() contains one y-column single-objective acquisition functions multiple y-columns multi-objective acquisition functions, e.g. data.table(y = 1:2) data.table(y1 = 1:2, y2 = 3:4).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Base Class — AcqFunction","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Optimizer — AcqOptimizer","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Optimizer AcqFunctions performs acquisition function optimization. Wraps bbotk::OptimizerBatch bbotk::Terminator.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Optimizer — AcqOptimizer","text":"n_candidates integer(1) Number candidate points propose. Note affect acquisition function calculated (e.g., setting n_candidates > 1 result computing q- multi-Expected Improvement) rather top n_candidates selected bbotk::ArchiveBatch acquisition function bbotk::OptimInstanceBatch. Note setting n_candidates > 1 usually sensible idea still supported experimental reasons. Note case acquisition function bbotk::OptimInstanceBatch multi-objective, due using AcqFunctionMulti, selection best candidates performed via non-dominated-sorting. Default 1. logging_level character(1) Logging level acquisition function optimization. Can \"fatal\", \"error\", \"warn\", \"info\", \"debug\" \"trace\". Default \"warn\", .e., warnings logged. warmstart logical(1) acquisition function optimization warm-started evaluating best point(s) present bbotk::Archive actual bbotk::OptimInstance (contained archive AcqFunction)? sensible using population based acquisition function optimizer, e.g., local search mutation. Default FALSE. Note case bbotk::OptimInstance multi-objective, selection best point(s) performed via non-dominated-sorting. warmstart_size integer(1) | \"\" Number best points selected bbotk::Archive actual bbotk::OptimInstance used warm starting. Can either integer \"\" use available points. relevant warmstart = TRUE. Default 1. skip_already_evaluated logical(1) can happen candidate(s) resulting acquisition function optimization already evaluated actual bbotk::OptimInstance. candidate proposals ignored candidates yet evaluated considered? Default TRUE. catch_errors logical(1) errors acquisition function optimization caught propagated loop_function can handle failed acquisition function optimization appropriately , e.g., proposing randomly sampled point evaluation? Setting FALSE can helpful debugging. Default TRUE.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Optimizer — AcqOptimizer","text":"optimizer (bbotk::OptimizerBatch). terminator (bbotk::Terminator). acq_function (AcqFunction). callbacks (NULL | list mlr3misc::Callback).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Optimizer — AcqOptimizer","text":"print_id (character) Id used printing. param_set (paradox::ParamSet) Set hyperparameters.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Optimizer — AcqOptimizer","text":"AcqOptimizer$new() AcqOptimizer$format() AcqOptimizer$print() AcqOptimizer$optimize() AcqOptimizer$reset() AcqOptimizer$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$new(optimizer, terminator, acq_function = NULL, callbacks = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Optimizer — AcqOptimizer","text":"optimizer (bbotk::OptimizerBatch). terminator (bbotk::Terminator). acq_function (NULL | AcqFunction). callbacks (NULL | list mlr3misc::Callback)","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-format-","dir":"Reference","previous_headings":"","what":"Method format()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Helper print outputs.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$format()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Optimizer — AcqOptimizer","text":"(character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Optimizer — AcqOptimizer","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Optimize acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$optimize()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Optimizer — AcqOptimizer","text":"data.table::data.table() 1 row per candidate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Reset acquisition function optimizer. Currently used.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Optimizer — AcqOptimizer","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"ei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 1000), terminator = trm(\"evals\", n_evals = 1000), acq_function = acq_function) acq_optimizer$optimize() } #> Loading required namespace: DiceKriging #> Loading required namespace: rgenoud #> x acq_ei x_domain .already_evaluated #> #> 1: 1.187665 5.305171 FALSE"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":null,"dir":"Reference","previous_headings":"","what":"Result Assigner Base Class — ResultAssigner","title":"Result Assigner Base Class — ResultAssigner","text":"Abstract result assigner class. result assigner responsible assigning final optimization result bbotk::OptimInstance. Normally, used within OptimizerMbo.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Result Assigner Base Class — ResultAssigner","text":"label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace().","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Result Assigner Base Class — ResultAssigner","text":"ResultAssigner$new() ResultAssigner$assign_result() ResultAssigner$format() ResultAssigner$print() ResultAssigner$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Result Assigner Base Class — ResultAssigner","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$new(label = NA_character_, man = NA_character_)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Base Class — ResultAssigner","text":"label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-assign-result-","dir":"Reference","previous_headings":"","what":"Method assign_result()","title":"Result Assigner Base Class — ResultAssigner","text":"Assigns result, .e., final point(s) instance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$assign_result(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Base Class — ResultAssigner","text":"instance (bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit |bbotk::OptimInstanceAsyncSingleCrit | bbotk::OptimInstanceAsyncMultiCrit) bbotk::OptimInstance final result assigned .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-format-","dir":"Reference","previous_headings":"","what":"Method format()","title":"Result Assigner Base Class — ResultAssigner","text":"Helper print outputs.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$format()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Result Assigner Base Class — ResultAssigner","text":"(character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Result Assigner Base Class — ResultAssigner","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Result Assigner Base Class — ResultAssigner","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Result Assigner Base Class — ResultAssigner","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Base Class — ResultAssigner","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":null,"dir":"Reference","previous_headings":"","what":"Surrogate Model — Surrogate","title":"Surrogate Model — Surrogate","text":"Abstract surrogate model class. surrogate model used model unknown objective function(s) based points evaluated far.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Surrogate Model — Surrogate","text":"learner (learner) Arbitrary learner object depending subclass.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Surrogate Model — Surrogate","text":"print_id (character) Id used printing. archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. archive_is_async (`bool(1)“) Whether bbotk::Archive asynchronous one. n_learner (integer(1)) Returns number surrogate models. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. cols_y (character() | NULL) Column id's variables used targets. default, automatically inferred based archive. insample_perf (numeric()) Surrogate model's current insample performance. param_set (paradox::ParamSet) Set hyperparameters. assert_insample_perf (numeric()) Asserts whether current insample performance meets performance threshold. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace(). feature_types (character()) Stores feature types surrogate can handle, e.g. \"logical\", \"numeric\", \"factor\". complete list candidate feature types, grouped task type, stored mlr_reflections$task_feature_types. properties (character()) Stores set properties/capabilities surrogate . complete list candidate properties, grouped task type, stored mlr_reflections$learner_properties. predict_type (character(1)) Retrieves currently active predict type, e.g. \"response\".","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Surrogate Model — Surrogate","text":"Surrogate$new() Surrogate$update() Surrogate$reset() Surrogate$predict() Surrogate$format() Surrogate$print() Surrogate$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Surrogate Model — Surrogate","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$new(learner, archive, cols_x, cols_y, param_set)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model — Surrogate","text":"learner (learner) Arbitrary learner object depending subclass. archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. cols_y (character() | NULL) Column id's variables used targets. default, automatically inferred based archive. param_set (paradox::ParamSet) Parameter space description depending subclass.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Surrogate Model — Surrogate","text":"Train learner new data. Subclasses must implement private.update() private.update_async().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Surrogate Model — Surrogate","text":"Reset surrogate model. Subclasses must implement private$.reset().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"NULL","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-predict-","dir":"Reference","previous_headings":"","what":"Method predict()","title":"Surrogate Model — Surrogate","text":"Predict mean response standard error. Must implemented subclasses.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$predict(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model — Surrogate","text":"xdt (data.table::data.table()) New data. One row per observation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"Arbitrary prediction object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-format-","dir":"Reference","previous_headings":"","what":"Method format()","title":"Surrogate Model — Surrogate","text":"Helper print outputs.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$format()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"(character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Surrogate Model — Surrogate","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-4","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Surrogate Model — Surrogate","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model — Surrogate","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Surrogate Model Containing a Single Learner — SurrogateLearner","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"Surrogate model containing single mlr3::LearnerRegr.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"assert_insample_perf logical(1) insample performance mlr3::LearnerRegr asserted updating surrogate? assertion fails (.e., insample performance based perf_measure meet perf_threshold), error thrown. Default FALSE. perf_measure mlr3::MeasureRegr Performance measure use assert insample performance mlr3::LearnerRegr. relevant assert_insample_perf = TRUE. Default mlr3::mlr_measures_regr.rsq. perf_threshold numeric(1) Threshold insample performance mlr3::LearnerRegr asserted . relevant assert_insample_perf = TRUE. Default 0. catch_errors logical(1) errors updating surrogate caught propagated loop_function can handle failed acquisition function optimization (result failed surrogate) appropriately , e.g., proposing randomly sampled point evaluation? Default TRUE. impute_method character(1) Method impute missing values case updating asynchronous bbotk::ArchiveAsync pending evaluations. Can \"mean\" use mean imputation \"random\" sample values uniformly random empirical minimum maximum. Default \"random\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"mlr3mbo::Surrogate -> SurrogateLearner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"print_id (character) Id used printing. n_learner (integer(1)) Returns number surrogate models. assert_insample_perf (numeric()) Asserts whether current insample performance meets performance threshold. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace(). feature_types (character()) Stores feature types surrogate can handle, e.g. \"logical\", \"numeric\", \"factor\". complete list candidate feature types, grouped task type, stored mlr_reflections$task_feature_types. properties (character()) Stores set properties/capabilities surrogate . complete list candidate properties, grouped task type, stored mlr_reflections$learner_properties. predict_type (character(1)) Retrieves currently active predict type, e.g. \"response\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"mlr3mbo::Surrogate$format() mlr3mbo::Surrogate$print() mlr3mbo::Surrogate$reset() mlr3mbo::Surrogate$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"SurrogateLearner$new() SurrogateLearner$predict() SurrogateLearner$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"SurrogateLearner$new(learner, archive = NULL, cols_x = NULL, col_y = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"learner (mlr3::LearnerRegr). archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. col_y (character(1) | NULL) Column id variable used target. default, automatically inferred based archive.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"method-predict-","dir":"Reference","previous_headings":"","what":"Method predict()","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"Predict mean response standard error.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"SurrogateLearner$predict(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"xdt (data.table::data.table()) New data. One row per observation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"data.table::data.table() columns mean se.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"SurrogateLearner$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) xdt = generate_design_random(instance$search_space, n = 4)$data instance$eval_batch(xdt) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) surrogate$update() surrogate$learner$model } #> #> Call: #> DiceKriging::km(design = data, response = task$truth(), covtype = \"matern5_2\", #> nugget = 2.83305750865222e-07, optim.method = \"gen\", control = pv$control) #> #> Trend coeff.: #> Estimate #> (Intercept) 5.8590 #> #> Covar. type : matern5_2 #> Covar. coeff.: #> Estimate #> theta(x) 1.1710 #> #> Variance estimate: 21.95183 #> #> Nugget effect : 2.833058e-07 #>"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":null,"dir":"Reference","previous_headings":"","what":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"Surrogate model containing multiple mlr3::LearnerRegr. mlr3::LearnerRegr fit target variables indicated via cols_y. Note redundant mlr3::LearnerRegr must deep clones.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"assert_insample_perf logical(1) insample performance mlr3::LearnerRegr asserted updating surrogate? assertion fails (.e., insample performance based perf_measure meet perf_threshold), error thrown. Default FALSE. perf_measure List mlr3::MeasureRegr Performance measures use assert insample performance mlr3::LearnerRegr. relevant assert_insample_perf = TRUE. Default mlr3::mlr_measures_regr.rsq learner. perf_threshold List numeric(1) Thresholds insample performance mlr3::LearnerRegr asserted . relevant assert_insample_perf = TRUE. Default 0 learner. catch_errors logical(1) errors updating surrogate caught propagated loop_function can handle failed acquisition function optimization (result failed surrogate) appropriately , e.g., proposing randomly sampled point evaluation? Default TRUE. impute_method character(1) Method impute missing values case updating asynchronous bbotk::ArchiveAsync pending evaluations. Can \"mean\" use mean imputation \"random\" sample values uniformly random empirical minimum maximum. Default \"random\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"mlr3mbo::Surrogate -> SurrogateLearnerCollection","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"print_id (character) Id used printing. n_learner (integer(1)) Returns number surrogate models. assert_insample_perf (numeric()) Asserts whether current insample performance meets performance threshold. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace(). feature_types (character()) Stores feature types surrogate can handle, e.g. \"logical\", \"numeric\", \"factor\". complete list candidate feature types, grouped task type, stored mlr_reflections$task_feature_types. properties (character()) Stores set properties/capabilities surrogate . complete list candidate properties, grouped task type, stored mlr_reflections$learner_properties. predict_type (character(1)) Retrieves currently active predict type, e.g. \"response\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"mlr3mbo::Surrogate$format() mlr3mbo::Surrogate$print() mlr3mbo::Surrogate$reset() mlr3mbo::Surrogate$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"SurrogateLearnerCollection$new() SurrogateLearnerCollection$predict() SurrogateLearnerCollection$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"SurrogateLearnerCollection$new( learners, archive = NULL, cols_x = NULL, cols_y = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"learners (list mlr3::LearnerRegr). archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. cols_y (character() | NULL) Column id's variables used targets. default, automatically inferred based archive.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"method-predict-","dir":"Reference","previous_headings":"","what":"Method predict()","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"Predict mean response standard error. Returns named list data.tables. contains mean response standard error one col_y.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"SurrogateLearnerCollection$predict(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"xdt (data.table::data.table()) New data. One row per observation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"list data.table::data.table()s columns mean se.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"SurrogateLearnerCollection$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\") & requireNamespace(\"ranger\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) xdt = generate_design_random(instance$search_space, n = 4)$data instance$eval_batch(xdt) learner1 = default_gp() learner2 = default_rf() surrogate = srlrn(list(learner1, learner2), archive = instance$archive) surrogate$update() surrogate$learner surrogate$learner[[\"y1\"]]$model surrogate$learner[[\"y2\"]]$model } #> Loading required namespace: ranger #> Ranger result #> #> Call: #> ranger::ranger(dependent.variable.name = task$target_names, data = task$data(), case.weights = task$weights$weight, keep.inbag = TRUE, num.threads = 1L, num.trees = 100L) #> #> Type: Regression #> Number of trees: 100 #> Sample size: 4 #> Number of independent variables: 1 #> Mtry: 1 #> Target node size: 5 #> Variable importance mode: none #> Splitrule: variance #> OOB prediction error (MSE): 4791.712 #> R squared (OOB): -0.404484"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Acquisition Function Construction — acqf","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"function complements mlr_acqfunctions functions spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"","code":"acqf(.key, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":".key (character(1)) Key passed respective dictionary retrieve object. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet, set public field. See mlr3misc::dictionary_sugar_get() details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"AcqFunction","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"","code":"acqf(\"ei\") #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: epsilon ParamDbl 0 Inf Inf 0 0"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Acquisition Functions Construction — acqfs","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"function complements mlr_acqfunctions functions spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"","code":"acqfs(.keys, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":".keys (character()) Keys passed respective dictionary retrieve multiple objects. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet, set public field. See mlr3misc::dictionary_sugar_get() details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"List AcqFunctions","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"","code":"acqfs(c(\"ei\", \"pi\", \"cb\")) #> $acq_ei #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: epsilon ParamDbl 0 Inf Inf 0 0 #> #> $acq_pi #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> #> $acq_cb #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: lambda ParamDbl 0 Inf Inf 2 2 #>"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"function allows construct AcqOptimizer spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"","code":"acqo(optimizer, terminator, acq_function = NULL, callbacks = NULL, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"optimizer (bbotk::OptimizerBatch)bbotk::OptimizerBatch used. terminator (bbotk::Terminator)bbotk::Terminator used. acq_function (NULL | AcqFunction)AcqFunction used. Can also NULL. callbacks (NULL | list mlr3misc::Callback) Callbacks used acquisition function optimization. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"AcqOptimizer","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"","code":"library(bbotk) acqo(opt(\"random_search\"), trm(\"evals\"), catch_errors = FALSE) #> : (OptimizerBatchRandomSearch | TerminatorEvals) #> * Parameters: n_candidates=1, logging_level=warn, warmstart=FALSE, #> skip_already_evaluated=TRUE, catch_errors=FALSE"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Acquisition Function — default_acqfunction","title":"Default Acquisition Function — default_acqfunction","text":"Chooses default acquisition function, .e. criterion used propose future points. synchronous single-objective optimization, defaults mlr_acqfunctions_ei. synchronous multi-objective optimization, defaults mlr_acqfunctions_smsego. asynchronous single-objective optimization, defaults mlr_acqfunctions_stochastic_cb.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Acquisition Function — default_acqfunction","text":"","code":"default_acqfunction(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Acquisition Function — default_acqfunction","text":"instance (bbotk::OptimInstance). object inherits bbotk::OptimInstance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Acquisition Function — default_acqfunction","text":"AcqFunction","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Acquisition Function Optimizer — default_acqoptimizer","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"Chooses default acquisition function optimizer. Defaults wrapping bbotk::OptimizerBatchRandomSearch allowing 10000 function evaluations (batch size 1000) via bbotk::TerminatorEvals.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"","code":"default_acqoptimizer(acq_function)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"acq_function (AcqFunction).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"AcqOptimizer","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Gaussian Process — default_gp","title":"Default Gaussian Process — default_gp","text":"helper function constructs default Gaussian Process mlr3::LearnerRegr example used default_surrogate. Constructs Kriging learner “\"regr.km\"” kernel “\"matern5_2\"”. noisy = FALSE (default) small nugget effect added nugget.stability = 10^-8 increase numerical stability hopefully prevent crashes DiceKriging. noisy = TRUE nugget effect estimated nugget.estim = TRUE. noisy = TRUE jitter set TRUE circumvent problem DiceKriging already trained input values produce exact trained output. general, instead default \"BFGS\" optimization method use rgenoud (\"gen\"), hybrid algorithm, combine global search based genetic algorithms local search based gradients. may improve model fit less frequently produce constant model prediction.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Gaussian Process — default_gp","text":"","code":"default_gp(noisy = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Gaussian Process — default_gp","text":"noisy (logical(1)) Whether learner used noisy objective function scenario. See .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Gaussian Process — default_gp","text":"mlr3::LearnerRegr","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Loop Function — default_loop_function","title":"Default Loop Function — default_loop_function","text":"Chooses default loop_function, .e. Bayesian Optimization flavor used optimization. single-objective optimization, defaults bayesopt_ego. multi-objective optimization, defaults bayesopt_smsego.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Loop Function — default_loop_function","text":"","code":"default_loop_function(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Loop Function — default_loop_function","text":"instance (bbotk::OptimInstance) object inherits bbotk::OptimInstance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Loop Function — default_loop_function","text":"loop_function","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Result Assigner — default_result_assigner","title":"Default Result Assigner — default_result_assigner","text":"Chooses default result assigner. Defaults ResultAssignerArchive.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Result Assigner — default_result_assigner","text":"","code":"default_result_assigner(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Result Assigner — default_result_assigner","text":"instance (bbotk::OptimInstance) object inherits bbotk::OptimInstance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Result Assigner — default_result_assigner","text":"ResultAssigner","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Random Forest — default_rf","title":"Default Random Forest — default_rf","text":"helper function constructs default random forest mlr3::LearnerRegr example used default_surrogate. Constructs ranger learner “\"regr.ranger\"” num.trees = 100, keep.inbag = TRUE se.method = \"jack\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Random Forest — default_rf","text":"","code":"default_rf(noisy = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Random Forest — default_rf","text":"noisy (logical(1)) Whether learner used noisy objective function scenario. Currently effect.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Random Forest — default_rf","text":"mlr3::LearnerRegr","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Surrogate — default_surrogate","title":"Default Surrogate — default_surrogate","text":"helper function constructs default Surrogate based properties bbotk::OptimInstance. numeric-(including integers) parameter spaces without dependencies Gaussian Process constricted via default_gp(). mixed numeric-categorical parameter spaces, spaces conditional parameters random forest constructed via default_rf(). case, learners encapsulated using “\"evaluate\"”, fallback learner set, cases surrogate learner errors. Currently, following learner used fallback: lrn(\"regr.ranger\", num.trees = 10L, keep.inbag = TRUE, se.method = \"jack\"). additionally dependencies present parameter space, inactive conditional parameters represented missing NA values training design data. simply handle imputation method, added random forest, concretely use po(\"imputesample\") (logicals) po(\"imputeoor\") (anything else) package mlr3pipelines. Characters always encoded factors via po(\"colapply\"). range imputation makes sense tree-based methods usually hard beat, see Ding et al. (2010). case dependencies, following learner used fallback: lrn(\"regr.featureless\"). n_learner 1, learner wrapped SurrogateLearner. Otherwise, n_learner larger 1, multiple deep clones learner wrapped SurrogateLearnerCollection.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Surrogate — default_surrogate","text":"","code":"default_surrogate( instance, learner = NULL, n_learner = NULL, force_random_forest = FALSE )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Surrogate — default_surrogate","text":"instance (bbotk::OptimInstance) object inherits bbotk::OptimInstance. learner (NULL | mlr3::Learner). specified, learner used instead defaults described . n_learner (NULL | integer(1)). Number learners considered construction Surrogate. specified based number objectives stated instance. force_random_forest (logical(1)). TRUE, random forest constructed even parameter space numeric-.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Surrogate — default_surrogate","text":"Surrogate","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Default Surrogate — default_surrogate","text":"Ding, Yufeng, Simonoff, S J (2010). “Investigation Missing Data Methods Classification Trees Applied Binary Response Data.” Journal Machine Learning Research, 11(1), 131–170.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/loop_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Loop Functions for Bayesian Optimization — loop_function","title":"Loop Functions for Bayesian Optimization — loop_function","text":"Loop functions determine behavior Bayesian Optimization algorithm global level. overview readily available loop functions, see .data.table(mlr_loop_functions). general, loop function simply decorated member S3 class loop_function. Attributes must include: id (id loop function), label (brief description), instance (\"single-crit\" \"multi_crit\"), man (link manual page). example, see, e.g., bayesopt_ego.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mbo_defaults.html","id":null,"dir":"Reference","previous_headings":"","what":"Defaults for OptimizerMbo — mbo_defaults","title":"Defaults for OptimizerMbo — mbo_defaults","text":"following defaults set OptimizerMbo optimization respective fields set initialization. Optimization Loop: default_loop_function Surrogate: default_surrogate Acquisition Function: default_acqfunction Acqfun Optimizer: default_acqoptimizer Result Assigner: default_result_assigner","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr3mbo-package.html","id":null,"dir":"Reference","previous_headings":"","what":"mlr3mbo: Flexible Bayesian Optimization — mlr3mbo-package","title":"mlr3mbo: Flexible Bayesian Optimization — mlr3mbo-package","text":"modern flexible approach Bayesian Optimization / Model Based Optimization building 'bbotk' package. 'mlr3mbo' toolbox providing ready--use optimization algorithms well fundamental building blocks allowing straightforward implementation custom algorithms. Single- multi-objective optimization supported well mixed continuous, categorical conditional search spaces. Moreover, using 'mlr3mbo' hyperparameter optimization machine learning models within 'mlr3' ecosystem straightforward via 'mlr3tuning'. Examples ready--use optimization algorithms include Efficient Global Optimization Jones et al. (1998) doi:10.1023/:1008306431147 , ParEGO Knowles (2006) doi:10.1109/TEVC.2005.851274 SMS-EGO Ponweiser et al. (2008) doi:10.1007/978-3-540-87700-4_78 .","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr3mbo-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"mlr3mbo: Flexible Bayesian Optimization — mlr3mbo-package","text":"Maintainer: Lennart Schneider lennart.sch@web.de (ORCID) Authors: Jakob Richter jakob1richter@gmail.com (ORCID) Marc Becker marcbecker@posteo.de (ORCID) Michel Lang michellang@gmail.com (ORCID) Bernd Bischl bernd_bischl@gmx.net (ORCID) Florian Pfisterer pfistererf@googlemail.com (ORCID) Martin Binder mlr.developer@mb706.com Sebastian Fischer sebf.fischer@gmail.com (ORCID) contributors: Michael H. Buselli [copyright holder] Wessel Dankers [copyright holder] Carlos Fonseca [copyright holder] Manuel Lopez-Ibanez [copyright holder] Luis Paquete [copyright holder]","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of Acquisition Functions — mlr_acqfunctions","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"simple mlr3misc::Dictionary storing objects class AcqFunction. acquisition function associated help page, see mlr_acqfunctions_[id]. convenient way retrieve construct acquisition function, see acqf() acqfs().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"R6::R6Class object inheriting mlr3misc::Dictionary.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"See mlr3misc::Dictionary.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"","code":"library(data.table) as.data.table(mlr_acqfunctions) #> Key: #> key label #> #> 1: aei Augmented Expected Improvement #> 2: cb Lower / Upper Confidence Bound #> 3: ehvi Expected Hypervolume Improvement #> 4: ehvigh Expected Hypervolume Improvement via GH Quadrature #> 5: ei Expected Improvement #> 6: eips Expected Improvement Per Second #> 7: mean Posterior Mean #> 8: multi Acquisition Function Wrapping Multiple Acquisition Functions #> 9: pi Probability Of Improvement #> 10: sd Posterior Standard Deviation #> 11: smsego SMS-EGO #> 12: stochastic_cb Stochastic Lower / Upper Confidence Bound #> 13: stochastic_ei Stochastic Expected Improvement #> man #> #> 1: mlr3mbo::mlr_acqfunctions_aei #> 2: mlr3mbo::mlr_acqfunctions_cb #> 3: mlr3mbo::mlr_acqfunctions_ehvi #> 4: mlr3mbo::mlr_acqfunctions_ehvigh #> 5: mlr3mbo::mlr_acqfunctions_ei #> 6: mlr3mbo::mlr_acqfunctions_eips #> 7: mlr3mbo::mlr_acqfunctions_mean #> 8: mlr3mbo::mlr_acqfunctions_multi #> 9: mlr3mbo::mlr_acqfunctions_pi #> 10: mlr3mbo::mlr_acqfunctions_sd #> 11: mlr3mbo::mlr_acqfunctions_smsego #> 12: mlr3mbo::mlr_acqfunctions_stochastic_cb #> 13: mlr3mbo::mlr_acqfunctions_stochastic_ei acqf(\"ei\") #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: epsilon ParamDbl 0 Inf Inf 0 0"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Augmented Expected Improvement. Useful working noisy objectives. Currently works correctly \"regr.km\" surrogate model nugget.estim = TRUE given.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"aei\") acqf(\"aei\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"\"c\" (numeric(1)) Constant \\(c\\) used Formula (14) Huang (2012) reflect degree risk aversion. Defaults 1.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Huang D, Allen TT, Notz WI, Zheng N (2012). “Erratum : Global Optimization Stochastic Black-box Systems via Sequential Kriging Meta-Models.” Journal Global Optimization, 54(2), 431–431.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionAEI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"y_effective_best (numeric(1)) Best effective objective value observed far. case maximization, already includes necessary change sign. noise_var (numeric(1)) Estimate variance noise. corresponds nugget estimate using mlr3learners surrogate model.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"AcqFunctionAEI$new() AcqFunctionAEI$update() AcqFunctionAEI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"AcqFunctionAEI$new(surrogate = NULL, c = 1)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"surrogate (NULL | SurrogateLearner). c (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Update acquisition function set y_effective_best noise_var.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"AcqFunctionAEI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"AcqFunctionAEI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) set.seed(2906) fun = function(xs) { list(y = xs$x ^ 2 + rnorm(length(xs$x), mean = 0, sd = 1)) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain, properties = \"noisy\") instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = lrn(\"regr.km\", covtype = \"matern5_2\", optim.method = \"gen\", nugget.estim = TRUE, jitter = 1e-12, control = list(trace = FALSE)) surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"aei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_aei #> #> 1: 7.583607 #> 2: 7.583607 #> 3: 7.583607"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"Lower / Upper Confidence Bound.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"cb\") acqf(\"cb\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"\"lambda\" (numeric(1)) \\(\\lambda\\) value used confidence bound. Defaults 2.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionCB","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset() mlr3mbo::AcqFunction$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"AcqFunctionCB$new() AcqFunctionCB$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"","code":"AcqFunctionCB$new(surrogate = NULL, lambda = 2)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"surrogate (NULL | SurrogateLearner). lambda (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"","code":"AcqFunctionCB$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"cb\", surrogate = surrogate, lambda = 3) acq_function$surrogate$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_cb #> #> 1: -55.38831 #> 2: -55.57158 #> 3: -49.63773"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Exact Expected Hypervolume Improvement. Calculates exact expected hypervolume improvement case two objectives. case optimizing two objective functions, AcqFunctionEHVIGH can used. See Emmerich et al. (2016) details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Emmerich, Michael, Yang, Kaifeng, Deutz, André, Wang, Hao, Fonseca, M. C (2016). “Multicriteria Generalization Bayesian Global Optimization.” Pardalos, M. P, Zhigljavsky, Anatoly, Žilinskas, Julius (eds.), Advances Stochastic Deterministic Global Optimization, 229–242. Springer International Publishing, Cham.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEHVI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"ys_front (matrix()) Approximated Pareto front. Sorted first objective. Signs corrected respect assuming minimization objectives. ref_point (numeric()) Reference point. Signs corrected respect assuming minimization objectives. ys_front_augmented (matrix()) Augmented approximated Pareto front. Sorted first objective. Signs corrected respect assuming minimization objectives.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"AcqFunctionEHVI$new() AcqFunctionEHVI$update() AcqFunctionEHVI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"AcqFunctionEHVI$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"surrogate (NULL | SurrogateLearnerCollection).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Update acquisition function set ys_front ref_point.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"AcqFunctionEHVI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"AcqFunctionEHVI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) acq_function = acqf(\"ehvi\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ehvi #> #> 1: 206.4260 #> 2: 264.3261 #> 3: 376.3486"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Expected Hypervolume Improvement. Computed via Gauss-Hermite quadrature. case optimizing two objective functions AcqFunctionEHVI preferred.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"\"k\" (integer(1)) Number nodes per objective used numerical integration via Gauss-Hermite quadrature. Defaults 15. example, two objectives optimized, total number nodes therefore 225 per default. Changing value construction requires call $update() update $gh_data field. \"r\" (numeric(1)) Pruning rate 0 1 determines fraction nodes Gauss-Hermite quadrature rule ignored based weight value (nodes lowest weights ignored). Default 0.2. Changing value construction require call $update().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Rahat, Alma, Chugh, Tinkle, Fieldsend, Jonathan, Allmendinger, Richard, Miettinen, Kaisa (2022). “Efficient Approximation Expected Hypervolume Improvement using Gauss-Hermit Quadrature.” Rudolph, Günter, Kononova, V. , Aguirre, Hernán, Kerschke, Pascal, Ochoa, Gabriela, Tušar, Tea (eds.), Parallel Problem Solving Nature – PPSN XVII, 90–103.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEHVIGH","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"ys_front (matrix()) Approximated Pareto front. Signs corrected respect assuming minimization objectives. ref_point (numeric()) Reference point. Signs corrected respect assuming minimization objectives. hypervolume (numeric(1)). Current hypervolume approximated Pareto front respect reference point. gh_data (matrix()) Data required Gauss-Hermite quadrature rule form matrix dimension (k x 2). row corresponds one Gauss-Hermite node (column \"x\") corresponding weight (column \"w\"). Computed via fastGHQuad::gaussHermiteData. Nodes scaled factor sqrt(2) weights normalized sum one constraint.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"AcqFunctionEHVIGH$new() AcqFunctionEHVIGH$update() AcqFunctionEHVIGH$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"AcqFunctionEHVIGH$new(surrogate = NULL, k = 15L, r = 0.2)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"surrogate (NULL | SurrogateLearnerCollection). k (integer(1)). r (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Update acquisition function set ys_front, ref_point, hypervolume gh_data.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"AcqFunctionEHVIGH$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"AcqFunctionEHVIGH$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) acq_function = acqf(\"ehvigh\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ehvigh #> #> 1: 136.3082 #> 2: 152.4999 #> 3: 166.7487"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Expected Improvement.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"ei\") acqf(\"ei\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"\"epsilon\" (numeric(1)) \\(\\epsilon\\) value used determine amount exploration. Higher values result importance improvements predicted posterior mean decreasing relative importance potential improvements regions high predictive uncertainty. Defaults 0 (standard Expected Improvement).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization Expensive Black-Box Functions.” Journal Global optimization, 13(4), 455–492.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"AcqFunctionEI$new() AcqFunctionEI$update() AcqFunctionEI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"AcqFunctionEI$new(surrogate = NULL, epsilon = 0)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"surrogate (NULL | SurrogateLearner). epsilon (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Update acquisition function set y_best.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"AcqFunctionEI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"AcqFunctionEI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"ei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ei #> #> 1: 4.092188 #> 2: 4.549039 #> 3: 5.037109"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Expected Improvement per Second. assumed calculations performed bbotk::OptimInstanceBatchSingleCrit. Additionally target values codomain minimized maximized, bbotk::Objective bbotk::OptimInstanceBatchSingleCrit return time values. column names target variable time variable must passed cols_y order (target, time) constructing SurrogateLearnerCollection used surrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"eips\") acqf(\"eips\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEIPS","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"col_y (character(1)). col_time (character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"AcqFunctionEIPS$new() AcqFunctionEIPS$update() AcqFunctionEIPS$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"AcqFunctionEIPS$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"surrogate (NULL | SurrogateLearnerCollection).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Update acquisition function set y_best.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"AcqFunctionEIPS$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"AcqFunctionEIPS$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2, time = abs(xs$x)) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\"), time = p_dbl(tags = \"time\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) surrogate$cols_y = c(\"y\", \"time\") acq_function = acqf(\"eips\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_eips #> #> 1: 4.401246 #> 2: 4.864655 #> 3: 5.297142"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Mean — mlr_acqfunctions_mean","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"Posterior Mean.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"mean\") acqf(\"mean\")"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionMean","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset() mlr3mbo::AcqFunction$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"AcqFunctionMean$new() AcqFunctionMean$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"","code":"AcqFunctionMean$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"surrogate (NULL | SurrogateLearner).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"","code":"AcqFunctionMean$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"mean\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_mean #> #> 1: 25.87163 #> 2: 23.23378 #> 3: 17.57580"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"Wrapping multiple AcqFunctions resulting multi-objective acquisition function composed individual ones. Note optimization direction wrapped acquisition function corrected maximization. acquisition function, Surrogate must used. acquisition functions passed construction already initialized surrogate, checked whether surrogate acquisition functions. acquisition functions initialized surrogate, surrogate passed construction lazy initialization used acquisition functions. optimization, AcqOptimizer can used AcqFunction, however, bbotk::OptimizerBatch wrapped within AcqOptimizer must support multi-objective optimization indicated via multi-crit property.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"multi\") acqf(\"multi\")"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionMulti","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"surrogate (Surrogate) Surrogate. acq_functions (list AcqFunction) Points list individual acquisition functions. acq_function_ids (character()) Points ids individual acquisition functions.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"AcqFunctionMulti$new() AcqFunctionMulti$update() AcqFunctionMulti$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"AcqFunctionMulti$new(acq_functions, surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"acq_functions (list AcqFunctions). surrogate (NULL | Surrogate).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"Update wrapped acquisition functions.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"AcqFunctionMulti$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"AcqFunctionMulti$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"multi\", acq_functions = acqfs(c(\"ei\", \"pi\", \"cb\")), surrogate = surrogate ) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ei acq_pi acq_cb #> #> 1: 4.400964 0.2666736 28.30071 #> 2: 4.864368 0.2939486 29.30224 #> 3: 5.296907 0.3509359 27.23262"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Probability Improvement.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"pi\") acqf(\"pi\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Kushner, J. H (1964). “New Method Locating Maximum Point Arbitrary Multipeak Curve Presence Noise.” Journal Basic Engineering, 86(1), 97–106.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionPI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"AcqFunctionPI$new() AcqFunctionPI$update() AcqFunctionPI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"AcqFunctionPI$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"surrogate (NULL | SurrogateLearner).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Update acquisition function set y_best.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"AcqFunctionPI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"AcqFunctionPI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"pi\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_pi #> #> 1: 0.2666813 #> 2: 0.2939562 #> 3: 0.3509427"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"Posterior Standard Deviation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"sd\") acqf(\"sd\")"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionSD","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset() mlr3mbo::AcqFunction$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"AcqFunctionSD$new() AcqFunctionSD$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"","code":"AcqFunctionSD$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"surrogate (NULL | SurrogateLearner).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"","code":"AcqFunctionSD$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"sd\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_sd #> #> 1: 27.08674 #> 2: 26.26854 #> 3: 22.40456"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"S-Metric Selection Evolutionary Multi-Objective Optimization Algorithm Acquisition Function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"\"lambda\" (numeric(1)) \\(\\lambda\\) value used confidence bound. Defaults 1. Based confidence = (1 - 2 * dnorm(lambda)) ^ m can calculate lambda given confidence level, see Ponweiser et al. (2008). \"epsilon\" (numeric(1)) \\(\\epsilon\\) used additive epsilon dominance. Can either single numeric value > 0 NULL (default). case NULL, epsilon vector maintained dynamically described Horn et al. (2015).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"acquisition function always also returns current epsilon values list column (acq_epsilon). values logged bbotk::ArchiveBatch bbotk::OptimInstanceBatch AcqOptimizer therefore also bbotk::Archive actual bbotk::OptimInstance optimized.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Ponweiser, Wolfgang, Wagner, Tobias, Biermann, Dirk, Vincze, Markus (2008). “Multiobjective Optimization Limited Budget Evaluations Using Model-Assisted S-Metric Selection.” Proceedings 10th International Conference Parallel Problem Solving Nature, 784–794. Horn, Daniel, Wagner, Tobias, Biermann, Dirk, Weihs, Claus, Bischl, Bernd (2015). “Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox Benchmark.” International Conference Evolutionary Multi-Criterion Optimization, 64–78.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionSmsEgo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"ys_front (matrix()) Approximated Pareto front. Signs corrected respect assuming minimization objectives. ref_point (numeric()) Reference point. Signs corrected respect assuming minimization objectives. epsilon (numeric()) Epsilon used additive epsilon dominance. progress (numeric(1)) Optimization progress (typically, number function evaluations left). Note requires bbotk::OptimInstanceBatch terminated via bbotk::TerminatorEvals.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"AcqFunctionSmsEgo$new() AcqFunctionSmsEgo$update() AcqFunctionSmsEgo$reset() AcqFunctionSmsEgo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$new(surrogate = NULL, lambda = 1, epsilon = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"surrogate (NULL | SurrogateLearnerCollection). lambda (numeric(1)). epsilon (NULL | numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Update acquisition function set ys_front, ref_point epsilon.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Reset acquisition function. Resets epsilon.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) acq_function = acqf(\"smsego\", surrogate = surrogate) acq_function$surrogate$update() acq_function$progress = 5 - 4 # n_evals = 5 and 4 points already evaluated acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_smsego acq_epsilon #> #> 1: -581.3330 0,0 #> 2: -730.5685 0,0 #> 3: -1132.3435 0,0"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Lower / Upper Confidence Bound lambda sampling decay. initial \\(\\lambda\\) drawn uniform distribution min_lambda max_lambda exponential distribution rate 1 / lambda. \\(\\lambda\\) updated update formula lambda * exp(-rate * (t %% period)), t number times acquisition function updated. acquisition function usually used within asynchronous optimizer, e.g., OptimizerAsyncMbo, can principle also used synchronous optimizers, e.g., OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"stochastic_cb\") acqf(\"stochastic_cb\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"\"lambda\" (numeric(1)) \\(\\lambda\\) value sampling exponential distribution. Defaults 1.96. \"min_lambda\" (numeric(1)) Minimum value \\(\\lambda\\)sampling uniform distribution. Defaults 0.01. \"max_lambda\" (numeric(1)) Maximum value \\(\\lambda\\) sampling uniform distribution. Defaults 10. \"distribution\" (character(1)) Distribution sample \\(\\lambda\\) . One c(\"uniform\", \"exponential\"). Defaults uniform. \"rate\" (numeric(1)) Rate exponential decay. Defaults 0 .e. decay. \"period\" (integer(1)) Period exponential decay. Defaults NULL, .e., decay period.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"acquisition function always also returns current (acq_lambda) original (acq_lambda_0) \\(\\lambda\\). values logged bbotk::ArchiveBatch bbotk::OptimInstanceBatch AcqOptimizer therefore also bbotk::Archive actual bbotk::OptimInstance optimized.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959. Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization Large Scale Hyperparameter Optimization.” 2023 IEEE 19th International Conference e-Science (e-Science), 1–10.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionStochasticCB","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"AcqFunctionStochasticCB$new() AcqFunctionStochasticCB$update() AcqFunctionStochasticCB$reset() AcqFunctionStochasticCB$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$new( surrogate = NULL, lambda = 1.96, min_lambda = 0.01, max_lambda = 10, distribution = \"uniform\", rate = 0, period = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"surrogate (NULL | SurrogateLearner). lambda (numeric(1)). min_lambda (numeric(1)). max_lambda (numeric(1)). distribution (character(1)). rate (numeric(1)). period (NULL | integer(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Update acquisition function. Samples decays lambda.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Reset acquisition function. Resets private update counter .t used within epsilon decay.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"stochastic_cb\", surrogate = surrogate, lambda = 3) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_cb acq_lambda acq_lambda_0 #> #> 1: -165.5554 7.067187 7.067187 #> 2: -162.4109 7.067187 7.067187 #> 3: -140.7614 7.067187 7.067187"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Expected Improvement epsilon decay. \\(\\epsilon\\) updated update formula epsilon * exp(-rate * (t %% period)) t number times acquisition function updated. acquisition function usually used within asynchronous optimizer, e.g., OptimizerAsyncMbo, can principle also used synchronous optimizers, e.g., OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"stochastic_ei\") acqf(\"stochastic_ei\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"\"epsilon\" (numeric(1)) \\(\\epsilon\\) value used determine amount exploration. Higher values result importance improvements predicted posterior mean decreasing relative importance potential improvements regions high predictive uncertainty. Defaults 0.1. \"rate\" (numeric(1)) Defaults 0.05. \"period\" (integer(1)) Period exponential decay. Defaults NULL, .e., decay period.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"acquisition function always also returns current (acq_epsilon) original (acq_epsilon_0) \\(\\epsilon\\). values logged bbotk::ArchiveBatch bbotk::OptimInstanceBatch AcqOptimizer therefore also bbotk::Archive actual bbotk::OptimInstance optimized.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization Expensive Black-Box Functions.” Journal Global optimization, 13(4), 455–492.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionStochasticEI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"AcqFunctionStochasticEI$new() AcqFunctionStochasticEI$update() AcqFunctionStochasticEI$reset() AcqFunctionStochasticEI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$new( surrogate = NULL, epsilon = 0.1, rate = 0.05, period = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"surrogate (NULL | SurrogateLearner). epsilon (numeric(1)). rate (numeric(1)). period (NULL | integer(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Update acquisition function. Sets y_best best observed objective function value. Decays epsilon.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Reset acquisition function. Resets private update counter .t used within epsilon decay.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"stochastic_ei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ei acq_epsilon acq_epsilon_0 #> #> 1: 4.374607 0.1 0.1 #> 2: 4.835292 0.1 0.1 #> 3: 5.262107 0.1 0.1"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of Loop Functions — mlr_loop_functions","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"simple mlr3misc::Dictionary storing objects class loop_function. loop function associated help page, see mlr_loop_functions_[id]. Retrieves object key key dictionary. Additional arguments must named passed constructor stored object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"R6::R6Class object inheriting mlr3misc::Dictionary.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"key (character(1)). ... () Passed constructor.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"Object corresponding key.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"See mlr3misc::Dictionary.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"","code":"library(data.table) as.data.table(mlr_loop_functions) #> Key: #> key label instance #> #> 1: bayesopt_ego Efficient Global Optimization single-crit #> 2: bayesopt_emo Multi-Objective EGO multi-crit #> 3: bayesopt_mpcl Multipoint Constant Liar single-crit #> 4: bayesopt_parego ParEGO multi-crit #> 5: bayesopt_smsego SMS-EGO multi-crit #> man #> #> 1: mlr3mbo::mlr_loop_functions_ego #> 2: mlr3mbo::mlr_loop_functions_emo #> 3: mlr3mbo::mlr_loop_functions_mpcl #> 4: mlr3mbo::mlr_loop_functions_parego #> 5: mlr3mbo::mlr_loop_functions_smsego"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"Loop function sequential single-objective Bayesian Optimization. Normally used inside OptimizerMbo. iteration initial design, surrogate acquisition function updated next candidate chosen based optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"","code":"bayesopt_ego( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"instance (bbotk::OptimInstanceBatchSingleCrit) bbotk::OptimInstanceBatchSingleCrit optimized. surrogate (Surrogate)Surrogate used surrogate. Typically SurrogateLearner. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchSingleCrit.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization Expensive Black-Box Functions.” Journal Global optimization, 13(4), 455–492. Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) # expected improvement per second example fun = function(xs) { list(y = xs$x ^ 2, time = abs(xs$x)) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\"), time = p_dbl(tags = \"time\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance, n_learner = 2) surrogate$cols_y = c(\"y\", \"time\") optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acqf(\"eips\"), acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> x x_domain y #> #> 1: 1.55992 2.433351 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"Loop function sequential multi-objective Bayesian Optimization. Normally used inside OptimizerMbo. conceptual counterpart mlr_loop_functions_ego. iteration initial design, surrogate acquisition function updated next candidate chosen based optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"","code":"bayesopt_emo( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"instance (bbotk::OptimInstanceBatchMultiCrit) bbotk::OptimInstanceBatchMultiCrit optimized. surrogate (SurrogateLearnerCollection)SurrogateLearnerCollection used surrogate. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"ehvi\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_emo, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> x x_domain y1 y2 #> #> 1: 0.7722247 0.5963309 1.5074323 #> 2: 1.0853905 1.1780725 0.8365106 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":null,"dir":"Reference","previous_headings":"","what":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"Loop function single-objective Bayesian Optimization via multipoint constant liar. Normally used inside OptimizerMbo. iteration initial design, surrogate acquisition function updated. acquisition function optimized, find candidate instead evaluating candidate, objective function value obtained applying liar function previously obtained objective function values. repeated q - 1 times obtain total q candidates evaluated single batch.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"","code":"bayesopt_mpcl( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, q = 2L, liar = mean, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"instance (bbotk::OptimInstanceBatchSingleCrit) bbotk::OptimInstanceBatchSingleCrit optimized. surrogate (Surrogate)Surrogate used surrogate. Typically SurrogateLearner. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. q (integer(1)) Batch size > 1. Default 2. liar (function) function accepting numeric vector input returning single numeric output. Default mean. sensible functions include min (max, depending optimization direction). random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchSingleCrit. make use parallel evaluations case `q > 1, objective function bbotk::OptimInstanceBatchSingleCrit must implemented accordingly.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"Ginsbourger, David, Le Riche, Rodolphe, Carraro, Laurent (2008). “Multi-Points Criterion Deterministic Parallel Global Optimization Based Gaussian Processes.” Wang, Jialei, Clark, C. S, Liu, Eric, Frazier, . P (2020). “Parallel Bayesian Global Optimization Expensive Functions.” Operations Research, 68(6), 1850–1865.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 7)) surrogate = default_surrogate(instance) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_mpcl, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer, args = list(q = 3)) optimizer$optimize(instance) } #> x x_domain y #> #> 1: 2.053603 4.217285 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"Loop function multi-objective Bayesian Optimization via ParEGO. Normally used inside OptimizerMbo. iteration initial design, observed objective function values normalized q candidates obtained scalarizing values via augmented Tchebycheff function, updating surrogate respect scalarized values optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"","code":"bayesopt_parego( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, q = 1L, s = 100L, rho = 0.05, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"instance (bbotk::OptimInstanceBatchMultiCrit) bbotk::OptimInstanceBatchMultiCrit optimized. surrogate (SurrogateLearner)SurrogateLearner used surrogate. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. q (integer(1)) Batch size, .e., number candidates obtained single batch. Default 1. s (integer(1)) \\(s\\) Equation 1 Knowles (2006). Determines total number possible random weight vectors. Default 100. rho (numeric(1)) \\(\\rho\\) Equation 2 Knowles (2006) scaling linear part augmented Tchebycheff function. Default 0.05 random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit. scalarizations objective function values stored y_scal column bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit. make use parallel evaluations case `q > 1, objective function bbotk::OptimInstanceBatchMultiCrit must implemented accordingly.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"Knowles, Joshua (2006). “ParEGO: Hybrid Algorithm -Line Landscape Approximation Expensive Multiobjective Optimization Problems.” IEEE Transactions Evolutionary Computation, 10(1), 50–66.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance, n_learner = 1) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_parego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> WARN [09:58:15.435] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this #> WARN [09:58:15.460] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. #> x x_domain y1 y2 #> #> 1: 0.7590663 0.5761817 1.539916 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"Loop function sequential multi-objective Bayesian Optimization via SMS-EGO. Normally used inside OptimizerMbo. iteration initial design, surrogate acquisition function (mlr_acqfunctions_smsego) updated next candidate chosen based optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"","code":"bayesopt_smsego( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"instance (bbotk::OptimInstanceBatchMultiCrit) bbotk::OptimInstanceBatchMultiCrit optimized. surrogate (SurrogateLearnerCollection)SurrogateLearnerCollection used surrogate. acq_function (mlr_acqfunctions_smsego)mlr_acqfunctions_smsego used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit. Due iterative computation epsilon within mlr_acqfunctions_smsego, requires bbotk::Terminator bbotk::OptimInstanceBatchMultiCrit bbotk::TerminatorEvals.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"Beume N, Naujoks B, Emmerich M (2007). “SMS-EMOA: Multiobjective selection based dominated hypervolume.” European Journal Operational Research, 181(3), 1653–1669. Ponweiser, Wolfgang, Wagner, Tobias, Biermann, Dirk, Vincze, Markus (2008). “Multiobjective Optimization Limited Budget Evaluations Using Model-Assisted S-Metric Selection.” Proceedings 10th International Conference Parallel Problem Solving Nature, 784–794.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"smsego\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_smsego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> x x_domain y1 y2 #> #> 1: -0.08059442 0.006495461 4.328873142 #> 2: 1.95018192 3.803209514 0.002481841 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":null,"dir":"Reference","previous_headings":"","what":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"OptimizerADBO class implements Asynchronous Decentralized Bayesian Optimization (ADBO). ADBO variant Asynchronous Model Based Optimization (AMBO) uses AcqFunctionStochasticCB exponential lambda decay. Currently, single-objective optimization supported OptimizerADBO considered experimental feature API might subject changes.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"lambda parameter confidence bound acquisition function controls trade-exploration exploitation. large lambda value leads exploration, small lambda value leads exploitation. initial lambda value acquisition function used worker drawn exponential distribution rate 1 / lambda. ADBO can use periodic exponential decay reduce lambda periodically given time step t formula lambda * exp(-rate * (t %% period)). SurrogateLearner configured use random forest AcqOptimizer random search batch size 1000 budget 10000 evaluations.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"lambda numeric(1) Value used sampling lambda worker exponential distribution. rate numeric(1) Rate exponential decay. period integer(1) Period exponential decay. initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization Large Scale Hyperparameter Optimization.” 2023 IEEE 19th International Conference e-Science (e-Science), 1–10.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"bbotk::Optimizer -> bbotk::OptimizerAsync -> mlr3mbo::OptimizerAsyncMbo -> OptimizerADBO","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"bbotk::Optimizer$format() bbotk::Optimizer$help() mlr3mbo::OptimizerAsyncMbo$print() mlr3mbo::OptimizerAsyncMbo$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"OptimizerADBO$new() OptimizerADBO$optimize() OptimizerADBO$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"OptimizerADBO$new()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"Performs optimization bbotk::OptimInstanceAsyncSingleCrit termination. single evaluations written bbotk::ArchiveAsync. result written instance object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"OptimizerADBO$optimize(inst)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"inst (bbotk::OptimInstanceAsyncSingleCrit).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"data.table::data.table()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"OptimizerADBO$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceAsyncSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) optimizer = opt(\"adbo\", design_size = 4, n_workers = 2) optimizer$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Loading required namespace: rush #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"OptimizerAsyncMbo class implements Asynchronous Model Based Optimization (AMBO). AMBO starts multiple sequential MBO runs different workers. worker communicate asynchronously shared archive relying rush package. optimizer follows modular layout surrogate model, acquisition function, acquisition optimizer can changed. SurrogateLearner impute missing values due pending evaluations. stochastic AcqFunction, e.g., AcqFunctionStochasticEI AcqFunctionStochasticCB used create varying versions acquisition function worker, promoting different exploration-exploitation trade-offs. AcqOptimizer class remains consistent one used synchronous MBO. contrast OptimizerMbo, loop_function can specified determines AMBO flavor OptimizerAsyncMbo simply relies surrogate update, acquisition function update acquisition function optimization step internal loop. Currently, single-objective optimization supported OptimizerAsyncMbo considered experimental feature API might subject changes. Note general SurrogateLearner updated one final time available data optimization process terminated. However, certain scenarios always possible meaningful. therefore recommended manually inspect SurrogateLearner optimization used, e.g., visualization purposes make sure properly updated available data. final update SurrogateLearner performed successfully, warning logged. specifying ResultAssigner, one can alter final result determined optimization, e.g., simply based evaluations logged archive ResultAssignerArchive based Surrogate via ResultAssignerSurrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"archive","dir":"Reference","previous_headings":"","what":"Archive","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"bbotk::ArchiveAsync holds following additional columns specific AMBO algorithms: acq_function$id (numeric(1)) value acquisition function. \".already_evaluated\" (logical(1)) Whether point already evaluated. Depends skip_already_evaluated parameter AcqOptimizer. bbotk::ArchiveAsync contain evaluations prior optimization, initial design needed. initial_design parameter specified data.table, data used. Otherwise, NULL, initial design size design_size generated based generate_design sampling function. See also parameters .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"bbotk::Optimizer -> bbotk::OptimizerAsync -> OptimizerAsyncMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"bbotk::Optimizer$format() bbotk::Optimizer$help()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"OptimizerAsyncMbo$new() OptimizerAsyncMbo$print() OptimizerAsyncMbo$reset() OptimizerAsyncMbo$optimize() OptimizerAsyncMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Creates new instance R6 class. surrogate NULL acq_function$surrogate field populated, SurrogateLearner used. Otherwise, default_surrogate(instance) used. acq_function NULL acq_optimizer$acq_function field populated, AcqFunction used (therefore $surrogate populated; see ). Otherwise default_acqfunction(instance) used. acq_optimizer NULL, default_acqoptimizer(instance) used. Even already initialized, surrogate$archive field always overwritten bbotk::ArchiveAsync current bbotk::OptimInstanceAsyncSingleCrit optimized. information default values surrogate, acq_function, acq_optimizer result_assigner, see ?mbo_defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$new( id = \"async_mbo\", surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, result_assigner = NULL, param_set = NULL, label = \"Asynchronous Model Based Optimization\", man = \"mlr3mbo::OptimizerAsyncMbo\" )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"id (character(1)) Identifier new instance. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_set (paradox::ParamSet) Set control parameters. label (character(1)) Label object. Can used tables, plot text output instead ID. man (character(1)) String format [pkg]::[topic] pointing manual page object. referenced help package can opened via method $help().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Reset optimizer. Sets following fields NULL: surrogate, acq_function, acq_optimizer,result_assigner Resets parameter values design_size design_function defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Performs optimization bbotk::OptimInstanceAsyncSingleCrit termination. single evaluations written bbotk::ArchiveAsync. result written instance object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$optimize(inst)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"inst (bbotk::OptimInstanceAsyncSingleCrit).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"data.table::data.table()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceAsyncSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) optimizer = opt(\"async_mbo\", design_size = 4, n_workers = 2) optimizer$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"Model Based Optimization — mlr_optimizers_mbo","title":"Model Based Optimization — mlr_optimizers_mbo","text":"OptimizerMbo class implements Model Based Optimization (MBO). implementation follows modular layout relying loop_function determining MBO flavor used, e.g., bayesopt_ego sequential single-objective Bayesian Optimization, Surrogate, AcqFunction, e.g., mlr_acqfunctions_ei Expected Improvement AcqOptimizer. MBO algorithms iterative optimization algorithms make use continuously updated surrogate model built objective function. optimizing comparably cheap evaluate acquisition function defined surrogate prediction, next candidate chosen evaluation. Detailed descriptions different MBO flavors provided documentation respective loop_function. Termination handled via bbotk::Terminator part bbotk::OptimInstanceBatch optimized. Note general Surrogate updated one final time available data optimization process terminated. However, certain scenarios always possible meaningful, e.g., using bayesopt_parego() multi-objective optimization uses surrogate relies scalarization objectives. therefore recommended manually inspect Surrogate optimization used, e.g., visualization purposes make sure properly updated available data. final update Surrogate performed successfully, warning logged. specifying ResultAssigner, one can alter final result determined optimization, e.g., simply based evaluations logged archive ResultAssignerArchive based Surrogate via ResultAssignerSurrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"archive","dir":"Reference","previous_headings":"","what":"Archive","title":"Model Based Optimization — mlr_optimizers_mbo","text":"bbotk::ArchiveBatch holds following additional columns specific MBO algorithms: acq_function$id (numeric(1)) value acquisition function. \".already_evaluated\" (logical(1)) Whether point already evaluated. Depends skip_already_evaluated parameter AcqOptimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Model Based Optimization — mlr_optimizers_mbo","text":"bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Model Based Optimization — mlr_optimizers_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Model Based Optimization — mlr_optimizers_mbo","text":"bbotk::Optimizer$format() bbotk::Optimizer$help()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Model Based Optimization — mlr_optimizers_mbo","text":"OptimizerMbo$new() OptimizerMbo$print() OptimizerMbo$reset() OptimizerMbo$optimize() OptimizerMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Creates new instance R6 class. surrogate NULL acq_function$surrogate field populated, Surrogate used. Otherwise, default_surrogate(instance) used. acq_function NULL acq_optimizer$acq_function field populated, AcqFunction used (therefore $surrogate populated; see ). Otherwise default_acqfunction(instance) used. acq_optimizer NULL, default_acqoptimizer(instance) used. Even already initialized, surrogate$archive field always overwritten bbotk::ArchiveBatch current bbotk::OptimInstanceBatch optimized. information default values loop_function, surrogate, acq_function, acq_optimizer result_assigner, see ?mbo_defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$new( loop_function = NULL, surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, args = NULL, result_assigner = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Based Optimization — mlr_optimizers_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Model Based Optimization — mlr_optimizers_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Reset optimizer. Sets following fields NULL: loop_function, surrogate, acq_function, acq_optimizer, args, result_assigner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Performs optimization writes optimization result bbotk::OptimInstanceBatch. optimization result returned complete optimization path stored bbotk::ArchiveBatch bbotk::OptimInstanceBatch.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$optimize(inst)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Based Optimization — mlr_optimizers_mbo","text":"inst (bbotk::OptimInstanceBatch).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Model Based Optimization — mlr_optimizers_mbo","text":"data.table::data.table.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Based Optimization — mlr_optimizers_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) # single-objective EGO fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) # multi-objective ParEGO fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) optimizer = opt(\"mbo\", loop_function = bayesopt_parego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> WARN [09:58:18.312] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this #> WARN [09:58:18.312] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. #> x x_domain y1 y2 #> #> 1: 1.5279770 2.3347137 0.2228057 #> 2: -0.1195738 0.0142979 4.4925933 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of Result Assigners — mlr_result_assigners","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"simple mlr3misc::Dictionary storing objects class ResultAssigner. acquisition function associated help page, see mlr_result_assigners_[id]. convenient way retrieve construct acquisition function, see ras().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"R6::R6Class object inheriting mlr3misc::Dictionary.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"See mlr3misc::Dictionary.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"","code":"library(data.table) as.data.table(mlr_result_assigners) #> Key: #> key label man #> #> 1: archive Archive mlr3mbo::mlr_result_assigners_archive #> 2: surrogate Mean Surrogate Prediction mlr3mbo::mlr_result_assigners_surrogate ras(\"archive\") #> "},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":null,"dir":"Reference","previous_headings":"","what":"Result Assigner Based on the Archive — mlr_result_assigners_archive","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"Result assigner chooses final point(s) based evaluations bbotk::Archive. mimics default behavior bbotk::Optimizer.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"mlr3mbo::ResultAssigner -> ResultAssignerArchive","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"mlr3mbo::ResultAssigner$format() mlr3mbo::ResultAssigner$print()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"ResultAssignerArchive$new() ResultAssignerArchive$assign_result() ResultAssignerArchive$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"ResultAssignerArchive$new()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"method-assign-result-","dir":"Reference","previous_headings":"","what":"Method assign_result()","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"Assigns result, .e., final point(s) instance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"ResultAssignerArchive$assign_result(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"instance (bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit |bbotk::OptimInstanceAsyncSingleCrit | bbotk::OptimInstanceAsyncMultiCrit) bbotk::OptimInstance final result assigned .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"ResultAssignerArchive$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"result_assigner = ras(\"archive\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":null,"dir":"Reference","previous_headings":"","what":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"Result assigner chooses final point(s) based surrogate mean prediction evaluated points bbotk::Archive. especially useful case noisy objective functions. case operating bbotk::OptimInstanceBatchMultiCrit bbotk::OptimInstanceAsyncMultiCrit SurrogateLearnerCollection must use many learners objective functions.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"mlr3mbo::ResultAssigner -> ResultAssignerSurrogate","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"surrogate (Surrogate | NULL) surrogate. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"mlr3mbo::ResultAssigner$format() mlr3mbo::ResultAssigner$print()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"ResultAssignerSurrogate$new() ResultAssignerSurrogate$assign_result() ResultAssignerSurrogate$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"ResultAssignerSurrogate$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"surrogate (Surrogate | NULL) surrogate used predict mean evaluated points.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"method-assign-result-","dir":"Reference","previous_headings":"","what":"Method assign_result()","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"Assigns result, .e., final point(s) instance. $surrogate NULL, default_surrogate(instance) used also assigned $surrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"ResultAssignerSurrogate$assign_result(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"instance (bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit |bbotk::OptimInstanceAsyncSingleCrit | bbotk::OptimInstanceAsyncMultiCrit) bbotk::OptimInstance final result assigned .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"ResultAssignerSurrogate$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"result_assigner = ras(\"surrogate\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":null,"dir":"Reference","previous_headings":"","what":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"TunerADBO class implements Asynchronous Decentralized Bayesian Optimization (ADBO). ADBO variant Asynchronous Model Based Optimization (AMBO) uses AcqFunctionStochasticCB exponential lambda decay. minimal interface internally passing OptimizerAsyncMbo. additional information documentation see OptimizerAsyncMbo. Currently, single-objective optimization supported TunerADBO considered experimental feature API might subject changes.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization Large Scale Hyperparameter Optimization.” 2023 IEEE 19th International Conference e-Science (e-Science), 1–10.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"mlr3tuning::Tuner -> mlr3tuning::TunerAsync -> mlr3tuning::TunerAsyncFromOptimizerAsync -> TunerADBO","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"mlr3tuning::Tuner$format() mlr3tuning::Tuner$help() mlr3tuning::TunerAsyncFromOptimizerAsync$optimize()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"TunerADBO$new() TunerADBO$print() TunerADBO$reset() TunerADBO$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$new()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Reset tuner. Sets following fields NULL: surrogate, acq_function, acq_optimizer, result_assigner Resets parameter values design_size design_function defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(mlr3) library(mlr3tuning) # single-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measure = msr(\"classif.acc\") instance = TuningInstanceAsyncSingleCrit$new( task = task, learner = learner, resampling = resampling, measure = measure, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) tnr(\"adbo\", design_size = 4, n_workers = 2)$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"TunerAsyncMbo class implements Asynchronous Model Based Optimization (AMBO). minimal interface internally passing OptimizerAsyncMbo. additional information documentation see OptimizerAsyncMbo. Currently, single-objective optimization supported TunerAsyncMbo considered experimental feature API might subject changes.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"mlr3tuning::Tuner -> mlr3tuning::TunerAsync -> mlr3tuning::TunerAsyncFromOptimizerAsync -> TunerAsyncMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"mlr3tuning::Tuner$format() mlr3tuning::Tuner$help() mlr3tuning::TunerAsyncFromOptimizerAsync$optimize()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"TunerAsyncMbo$new() TunerAsyncMbo$print() TunerAsyncMbo$reset() TunerAsyncMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"Creates new instance R6 class. information default values surrogate, acq_function, acq_optimizer, result_assigner, see ?mbo_defaults. Note parameters simply passed OptimizerAsyncMbo respective fields simply (settable) active bindings fields OptimizerAsyncMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$new( surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, param_set = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. param_set (paradox::ParamSet) Set control parameters.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"Reset tuner. Sets following fields NULL: surrogate, acq_function, acq_optimizer, result_assigner Resets parameter values design_size design_function defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(mlr3) library(mlr3tuning) # single-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measure = msr(\"classif.acc\") instance = TuningInstanceAsyncSingleCrit$new( task = task, learner = learner, resampling = resampling, measure = measure, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) tnr(\"async_mbo\", design_size = 4, n_workers = 2)$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"TunerMbo class implements Model Based Optimization (MBO). minimal interface internally passing OptimizerMbo. additional information documentation see OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"mlr3tuning::Tuner$format() mlr3tuning::Tuner$help() mlr3tuning::TunerBatchFromOptimizerBatch$optimize()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"TunerMbo$new() TunerMbo$print() TunerMbo$reset() TunerMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"Creates new instance R6 class. information default values loop_function, surrogate, acq_function, acq_optimizer, result_assigner, see ?mbo_defaults. Note parameters simply passed OptimizerMbo respective fields simply (settable) active bindings fields OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$new( loop_function = NULL, surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, args = NULL, result_assigner = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"Reset tuner. Sets following fields NULL: loop_function, surrogate, acq_function, acq_optimizer, args, result_assigner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(mlr3) library(mlr3tuning) # single-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measure = msr(\"classif.acc\") instance = TuningInstanceBatchSingleCrit$new( task = task, learner = learner, resampling = resampling, measure = measure, terminator = trm(\"evals\", n_evals = 5)) tnr(\"mbo\")$optimize(instance) # multi-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measures = msrs(c(\"classif.acc\", \"selected_features\")) instance = TuningInstanceBatchMultiCrit$new( task = task, learner = learner, resampling = resampling, measures = measures, terminator = trm(\"evals\", n_evals = 5), store_models = TRUE) # required due to selected features tnr(\"mbo\")$optimize(instance) } #> cp learner_param_vals x_domain classif.acc selected_features #> #> 1: -3.599781 0.8594162 2.666667 #> 2: -8.204952 0.8594162 2.666667 #> 3: -1.297196 0.8142185 2.000000 #> 4: -5.902366 0.8594162 2.666667 #> 5: -1.305511 0.8142185 2.000000 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Result Assigner Construction — ras","title":"Syntactic Sugar Result Assigner Construction — ras","text":"function complements mlr_result_assigners functions spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Result Assigner Construction — ras","text":"","code":"ras(.key, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Result Assigner Construction — ras","text":".key (character(1)) Key passed respective dictionary retrieve object. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet, set public field. See mlr3misc::dictionary_sugar_get() details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Result Assigner Construction — ras","text":"ResultAssigner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Result Assigner Construction — ras","text":"","code":"ras(\"archive\") #> "},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if Redis Server is Available — redis_available","title":"Check if Redis Server is Available — redis_available","text":"Attempts establish connection Redis server using redux package sends PING command. Returns TRUE server available responds appropriately, FALSE otherwise.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if Redis Server is Available — redis_available","text":"","code":"redis_available()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if Redis Server is Available — redis_available","text":"(logical(1))","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if Redis Server is Available — redis_available","text":"","code":"if (redis_available()) { # Proceed with code that requires Redis message(\"Redis server is available.\") } else { message(\"Redis server is not available.\") } #> Redis server is not available."},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Surrogate Construction — srlrn","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"function allows construct SurrogateLearner SurrogateLearnerCollection spirit mlr_sugar mlr3. archive references one target variable cols_y contains one target variable single learner specified, learner replicated many times needed build SurrogateLearnerCollection.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"","code":"srlrn(learner, archive = NULL, cols_x = NULL, cols_y = NULL, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"learner (mlr3::LearnerRegr | List mlr3::LearnerRegr)mlr3::LearnerRegr used within SurrogateLearner list mlr3::LearnerRegr used within SurrogateLearnerCollection. archive (NULL | bbotk::Archive)bbotk::Archive bbotk::OptimInstance used. Can also NULL. cols_x (NULL | character()) Column ids bbotk::Archive used features. Can also NULL case automatically inferred based archive. cols_y (NULL | character()) Column id(s) bbotk::Archive used target. list mlr3::LearnerRegr provided learner argument cols_y specified well, many column names learners must provided. Can also NULL case automatically inferred based archive. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"SurrogateLearner | SurrogateLearnerCollection","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"","code":"library(mlr3) srlrn(lrn(\"regr.featureless\"), catch_errors = FALSE) #> : LearnerRegrFeatureless #> * Parameters: assert_insample_perf=FALSE, catch_errors=FALSE, #> impute_method=random srlrn(list(lrn(\"regr.featureless\"), lrn(\"regr.featureless\"))) #> : (LearnerRegrFeatureless | LearnerRegrFeatureless) #> * Parameters: assert_insample_perf=FALSE, catch_errors=TRUE, #> impute_method=random"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-028","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.8","title":"mlr3mbo 0.2.8","text":"CRAN release: 2024-11-21 docs: gracefully exit examples OptimizerAsyncMbo, OptimizerADBO, TunerAsyncMbo, TunerADBO Redis available. test: skip tests involving asynchronous logic Redis available.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-027","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.7","title":"mlr3mbo 0.2.7","text":"CRAN release: 2024-11-15 refactor: refactored SurrogateLearner SurrogateLearnerCollection allow updating asynchronous Archive. feat: added experimental OptimizerAsyncMbo, OptimizerADBO, TunerAsyncMbo, TunerADBO allow asynchronous optimization. feat: added AcqFunctionStochasticCB AcqFunctionStochasticEI useful asynchronous optimization. docs: minor changes highlight differences batch asynchronous objects related asynchronous support. refactor: AcqFunctions AcqOptimizer gained reset() method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-026","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.6","title":"mlr3mbo 0.2.6","text":"CRAN release: 2024-10-16 refactor: Extract internal tuned values instance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-025","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.5","title":"mlr3mbo 0.2.5","text":"CRAN release: 2024-09-24 docs: Move vignette mlr3book. feat: Add AcqFunctionMulti can wrap multiple acquisition functions resulting multi-objective acquisition function problem. feat: Support callbacks AcqOptimizer. feat: Allow AcqFunctionEI adjusted epsilon strengthen exploration.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-024","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.4","title":"mlr3mbo 0.2.4","text":"CRAN release: 2024-07-06 fix: Improve runtime AcqOptimizer setting check_values = FALSE.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-023","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.3","title":"mlr3mbo 0.2.3","text":"CRAN release: 2024-07-01 compatibility: Work new bbotk mlr3tuning version 1.0.0.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-022","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.2","title":"mlr3mbo 0.2.2","text":"CRAN release: 2024-03-01 refactor: compatibility upcoming paradox upgrade. feat: OptimizerMbo TunerMbo now update Surrogate final time optimization process finished ensure Surrogate correctly reflects state trained data seen optimization. fix: AcqFunction domain construction now respects Surrogate cols_x field. feat: support one candidate point result acquisition function optimization even non-batch acquisition functions. feat: added default_gp default_rf helpers allow construction default Gaussian Process random forest example used within default_surrogate. refactor: changed Gaussian Process random forest defaults (default_gp default_rf therefore also default_surrogate). Gaussian Process now uses \"matern5_2\" kernel. Random forest now uses 100 trees. number trees used fallback random forest reduced 10.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-021","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.1","title":"mlr3mbo 0.2.1","text":"CRAN release: 2023-06-05 docs: updated references vignette. refactor: minor clean internal structure loop functions. perf: default initial design constructed based Sobol sequence loop functions. refactor: longer depend mlr3tuning import instead. refactor: srlrn sugar function now can construct SurrogateLearner SurrogateLearnerCollection; dropped srlrnc. feat: added AcqFunctionSD, AcqFunctionEHVI AcqFunctionEHVIGH, introduced bayesopt_emo loop function. feat: AcqFunctions now include $packages field stating required packages checked whether namespace can loaded prior optimization. fix: fixed bug fix_xdt_missing() helper function. BREAKING CHANGE: renaming default_loopfun -> default_loop_function, default_acqfun -> default_acqfunction, default_acqopt -> default_acqoptimizer. BREAKING CHANGE: result_functions now replaced ResultAssigners. BREAKING CHANGE: renamed $model field Surrogate classes $learner. BREAKING CHANGE: Surrogate AcquisitionFunction classes fields *_cols renamed cols_* (e.g., x_cols cols_x).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-012","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.1.2","title":"mlr3mbo 0.1.2","text":"CRAN release: 2023-03-02 refactor: adapt mlr3tuning 0.18.0. feat: Acquisition functions now assert whether surrogates match required predict type. fix: Unloading mlr3mbo removes optimizers tuners dictionaries. docs: faster examples. feat: characters surrogate regression tasks longer automatically converted factors. default_surrogate now respects gained appropriate pipeline step. feat: AcqFunctionAEI added. docs: fix docs, README bibentries.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-011","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.1.1","title":"mlr3mbo 0.1.1","text":"CRAN release: 2022-11-18 Initial upload CRAN.","code":""}] +[{"path":"https://mlr3mbo.mlr-org.com/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Lennart Schneider. Maintainer, author. Jakob Richter. Author. Marc Becker. Author. Michel Lang. Author. Bernd Bischl. Author. Florian Pfisterer. Author. Martin Binder. Author. Sebastian Fischer. Author. Michael H. Buselli. Copyright holder. Wessel Dankers. Copyright holder. Carlos Fonseca. Copyright holder. Manuel Lopez-Ibanez. Copyright holder. Luis Paquete. Copyright holder.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Schneider L, Richter J, Becker M, Lang M, Bischl B, Pfisterer F, Binder M, Fischer S (2024). mlr3mbo: Flexible Bayesian Optimization. R package version 0.2.8.9000, https://github.com/mlr-org/mlr3mbo, https://mlr3mbo.mlr-org.com.","code":"@Manual{, title = {mlr3mbo: Flexible Bayesian Optimization}, author = {Lennart Schneider and Jakob Richter and Marc Becker and Michel Lang and Bernd Bischl and Florian Pfisterer and Martin Binder and Sebastian Fischer}, year = {2024}, note = {R package version 0.2.8.9000, https://github.com/mlr-org/mlr3mbo}, url = {https://mlr3mbo.mlr-org.com}, }"},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"mlr3mbo","dir":"","previous_headings":"","what":"Flexible Bayesian Optimization","title":"Flexible Bayesian Optimization","text":"Package website: release | dev new R6 much modular implementation single- multi-objective Bayesian Optimization.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get Started","title":"Flexible Bayesian Optimization","text":"best entry point get familiar mlr3mbo provided via Bayesian Optimization chapter mlr3book.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"design","dir":"","previous_headings":"","what":"Design","title":"Flexible Bayesian Optimization","text":"mlr3mbo built modular relying following R6 classes: Surrogate: Surrogate Model AcqFunction: Acquisition Function AcqOptimizer: Acquisition Function Optimizer Based , Bayesian Optimization (BO) loops can written, see, e.g., bayesopt_ego sequential single-objective BO. mlr3mbo also provides OptimizerMbo class behaving like Optimizer bbotk package well TunerMbo class behaving like Tuner mlr3tuning package. mlr3mbo uses sensible defaults Surrogate, AcqFunction, AcqOptimizer, even loop_function. See ?mbo_defaults details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"simple-optimization-example","dir":"","previous_headings":"","what":"Simple Optimization Example","title":"Flexible Bayesian Optimization","text":"Minimize two-dimensional Branin function via sequential BO using GP surrogate EI acquisition function optimized via local serch: can quickly visualize contours objective function (log scale) well sampling behavior BO run (lighter blue colours indicating points evaluated later stages optimization process; first batch given initial design). Note can also use bb_optimize shorthand instead constructing optimization instance.","code":"library(bbotk) library(mlr3mbo) library(mlr3learners) set.seed(1) fun = function(xdt) { y = branin(xdt[[\"x1\"]], xdt[[\"x2\"]]) data.table(y = y) } domain = ps( x1 = p_dbl(-5, 10), x2 = p_dbl(0, 15) ) codomain = ps( y = p_dbl(tags = \"minimize\") ) objective = ObjectiveRFunDt$new( fun = fun, domain = domain, codomain = codomain ) instance = oi( objective = objective, terminator = trm(\"evals\", n_evals = 25) ) surrogate = srlrn(lrn(\"regr.km\", control = list(trace = FALSE))) acq_function = acqf(\"ei\") acq_optimizer = acqo( opt(\"local_search\", n_initial_points = 10, initial_random_sample_size = 1000, neighbors_per_point = 10), terminator = trm(\"evals\", n_evals = 2000) ) optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer ) optimizer$optimize(instance) ## x1 x2 x_domain y ## ## 1: 3.104516 2.396279 0.412985 library(ggplot2) grid = generate_design_grid(instance$search_space, resolution = 1000L)$data grid[, y := branin(x1 = x1, x2 = x2)] ggplot(aes(x = x1, y = x2, z = log(y)), data = grid) + geom_contour(colour = \"black\") + geom_point(aes(x = x1, y = x2, colour = batch_nr), data = instance$archive$data) + labs(x = expression(x[1]), y = expression(x[2])) + theme_minimal() + theme(legend.position = \"bottom\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/index.html","id":"simple-tuning-example","dir":"","previous_headings":"","what":"Simple Tuning Example","title":"Flexible Bayesian Optimization","text":"","code":"library(mlr3) library(mlr3learners) library(mlr3tuning) library(mlr3mbo) set.seed(1) task = tsk(\"pima\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-04, upper = 1, logscale = TRUE)) instance = tune( tuner = tnr(\"mbo\"), task = task, learner = learner, resampling = rsmp(\"holdout\"), measure = msr(\"classif.ce\"), term_evals = 10) instance$result ## cp learner_param_vals x_domain classif.ce ## ## 1: -6.188733 0.2382812"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Base Class — AcqFunction","title":"Acquisition Function Base Class — AcqFunction","text":"Abstract acquisition function class. Based predictions Surrogate, acquisition function encodes preference evaluate new point.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Acquisition Function Base Class — AcqFunction","text":"bbotk::Objective -> AcqFunction","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Base Class — AcqFunction","text":"direction (\"\" | \"minimize\" | \"maximize\") Optimization direction acquisition function relative direction objective function bbotk::OptimInstance related passed bbotk::Archive. Must \"\", \"minimize\", \"maximize\". surrogate_max_to_min (-1 | 1) Multiplicative factor correct minimization maximization acquisition function. label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object. archive (bbotk::Archive) Points bbotk::Archive surrogate. fun (function) Points private acquisition function implemented subclasses. surrogate (Surrogate) Surrogate. requires_predict_type_se (logical(1)) Whether acquisition function requires surrogate \"se\" $predict_type. packages (character()) Set required packages.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Base Class — AcqFunction","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Base Class — AcqFunction","text":"AcqFunction$new() AcqFunction$update() AcqFunction$reset() AcqFunction$eval_many() AcqFunction$eval_dt() AcqFunction$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Base Class — AcqFunction","text":"Creates new instance R6 class. Note surrogate can initialized lazy can later set via active binding $surrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$new( id, constants = ParamSet$new(), surrogate, requires_predict_type_se, direction, packages = NULL, label = NA_character_, man = NA_character_ )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"id (character(1)). constants (paradox::ParamSet). Changeable constants parameters. surrogate (NULL | Surrogate). Surrogate whose predictions used acquisition function. requires_predict_type_se (logical(1)) Whether acquisition function requires surrogate \"se\" $predict_type. direction (\"\" | \"minimize\" | \"maximize\"). Optimization direction acquisition function relative direction objective function bbotk::OptimInstance. Must \"\", \"minimize\", \"maximize\". packages (character()) Set required packages. warning signaled prior construction least one packages installed, loaded (attached) later -demand via requireNamespace(). label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Base Class — AcqFunction","text":"Update acquisition function. Can implemented subclasses.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Base Class — AcqFunction","text":"Reset acquisition function. Can implemented subclasses.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-eval-many-","dir":"Reference","previous_headings":"","what":"Method eval_many()","title":"Acquisition Function Base Class — AcqFunction","text":"Evaluates multiple input values acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$eval_many(xss)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"xss (list()) list lists contains multiple x values, e.g. list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Base Class — AcqFunction","text":"data.table::data.table() contains one y-column single-objective acquisition functions multiple y-columns multi-objective acquisition functions, e.g. data.table(y = 1:2) data.table(y1 = 1:2, y2 = 3:4).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-eval-dt-","dir":"Reference","previous_headings":"","what":"Method eval_dt()","title":"Acquisition Function Base Class — AcqFunction","text":"Evaluates multiple input values objective function","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$eval_dt(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"xdt (data.table::data.table()) One point per row, e.g. data.table(x1 = c(1, 3), x2 = c(2, 4)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Base Class — AcqFunction","text":"data.table::data.table() contains one y-column single-objective acquisition functions multiple y-columns multi-objective acquisition functions, e.g. data.table(y = 1:2) data.table(y1 = 1:2, y2 = 3:4).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Base Class — AcqFunction","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Base Class — AcqFunction","text":"","code":"AcqFunction$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqFunction.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Base Class — AcqFunction","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Optimizer — AcqOptimizer","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Optimizer AcqFunctions performs acquisition function optimization. Wraps bbotk::OptimizerBatch bbotk::Terminator.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Optimizer — AcqOptimizer","text":"n_candidates integer(1) Number candidate points propose. Note affect acquisition function calculated (e.g., setting n_candidates > 1 result computing q- multi-Expected Improvement) rather top n_candidates selected bbotk::ArchiveBatch acquisition function bbotk::OptimInstanceBatch. Note setting n_candidates > 1 usually sensible idea still supported experimental reasons. Note case acquisition function bbotk::OptimInstanceBatch multi-objective, due using AcqFunctionMulti, selection best candidates performed via non-dominated-sorting. Default 1. logging_level character(1) Logging level acquisition function optimization. Can \"fatal\", \"error\", \"warn\", \"info\", \"debug\" \"trace\". Default \"warn\", .e., warnings logged. warmstart logical(1) acquisition function optimization warm-started evaluating best point(s) present bbotk::Archive actual bbotk::OptimInstance (contained archive AcqFunction)? sensible using population based acquisition function optimizer, e.g., local search mutation. Default FALSE. Note case bbotk::OptimInstance multi-objective, selection best point(s) performed via non-dominated-sorting. warmstart_size integer(1) | \"\" Number best points selected bbotk::Archive actual bbotk::OptimInstance used warm starting. Can either integer \"\" use available points. relevant warmstart = TRUE. Default 1. skip_already_evaluated logical(1) can happen candidate(s) resulting acquisition function optimization already evaluated actual bbotk::OptimInstance. candidate proposals ignored candidates yet evaluated considered? Default TRUE. catch_errors logical(1) errors acquisition function optimization caught propagated loop_function can handle failed acquisition function optimization appropriately , e.g., proposing randomly sampled point evaluation? Setting FALSE can helpful debugging. Default TRUE.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Optimizer — AcqOptimizer","text":"optimizer (bbotk::OptimizerBatch). terminator (bbotk::Terminator). acq_function (AcqFunction). callbacks (NULL | list mlr3misc::Callback).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Optimizer — AcqOptimizer","text":"print_id (character) Id used printing. param_set (paradox::ParamSet) Set hyperparameters.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Optimizer — AcqOptimizer","text":"AcqOptimizer$new() AcqOptimizer$format() AcqOptimizer$print() AcqOptimizer$optimize() AcqOptimizer$reset() AcqOptimizer$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$new(optimizer, terminator, acq_function = NULL, callbacks = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Optimizer — AcqOptimizer","text":"optimizer (bbotk::OptimizerBatch). terminator (bbotk::Terminator). acq_function (NULL | AcqFunction). callbacks (NULL | list mlr3misc::Callback)","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-format-","dir":"Reference","previous_headings":"","what":"Method format()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Helper print outputs.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$format()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Optimizer — AcqOptimizer","text":"(character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Optimizer — AcqOptimizer","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Optimize acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$optimize()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Acquisition Function Optimizer — AcqOptimizer","text":"data.table::data.table() 1 row per candidate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"Reset acquisition function optimizer. Currently used.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Optimizer — AcqOptimizer","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"AcqOptimizer$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Optimizer — AcqOptimizer","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/AcqOptimizer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Optimizer — AcqOptimizer","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"ei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 1000), terminator = trm(\"evals\", n_evals = 1000), acq_function = acq_function) acq_optimizer$optimize() } #> Loading required namespace: DiceKriging #> Loading required namespace: rgenoud #> x acq_ei x_domain .already_evaluated #> #> 1: 1.187665 5.305171 FALSE"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":null,"dir":"Reference","previous_headings":"","what":"Result Assigner Base Class — ResultAssigner","title":"Result Assigner Base Class — ResultAssigner","text":"Abstract result assigner class. result assigner responsible assigning final optimization result bbotk::OptimInstance. Normally, used within OptimizerMbo.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Result Assigner Base Class — ResultAssigner","text":"label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace().","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Result Assigner Base Class — ResultAssigner","text":"ResultAssigner$new() ResultAssigner$assign_result() ResultAssigner$format() ResultAssigner$print() ResultAssigner$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Result Assigner Base Class — ResultAssigner","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$new(label = NA_character_, man = NA_character_)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Base Class — ResultAssigner","text":"label (character(1)) Label object. man (character(1)) String format [pkg]::[topic] pointing manual page object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-assign-result-","dir":"Reference","previous_headings":"","what":"Method assign_result()","title":"Result Assigner Base Class — ResultAssigner","text":"Assigns result, .e., final point(s) instance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$assign_result(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Base Class — ResultAssigner","text":"instance (bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit |bbotk::OptimInstanceAsyncSingleCrit | bbotk::OptimInstanceAsyncMultiCrit) bbotk::OptimInstance final result assigned .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-format-","dir":"Reference","previous_headings":"","what":"Method format()","title":"Result Assigner Base Class — ResultAssigner","text":"Helper print outputs.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$format()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Result Assigner Base Class — ResultAssigner","text":"(character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Result Assigner Base Class — ResultAssigner","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Result Assigner Base Class — ResultAssigner","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Result Assigner Base Class — ResultAssigner","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Base Class — ResultAssigner","text":"","code":"ResultAssigner$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ResultAssigner.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Base Class — ResultAssigner","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":null,"dir":"Reference","previous_headings":"","what":"Surrogate Model — Surrogate","title":"Surrogate Model — Surrogate","text":"Abstract surrogate model class. surrogate model used model unknown objective function(s) based points evaluated far.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Surrogate Model — Surrogate","text":"learner (learner) Arbitrary learner object depending subclass.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Surrogate Model — Surrogate","text":"print_id (character) Id used printing. archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. archive_is_async (`bool(1)“) Whether bbotk::Archive asynchronous one. n_learner (integer(1)) Returns number surrogate models. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. cols_y (character() | NULL) Column id's variables used targets. default, automatically inferred based archive. insample_perf (numeric()) Surrogate model's current insample performance. param_set (paradox::ParamSet) Set hyperparameters. assert_insample_perf (numeric()) Asserts whether current insample performance meets performance threshold. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace(). feature_types (character()) Stores feature types surrogate can handle, e.g. \"logical\", \"numeric\", \"factor\". complete list candidate feature types, grouped task type, stored mlr_reflections$task_feature_types. properties (character()) Stores set properties/capabilities surrogate . complete list candidate properties, grouped task type, stored mlr_reflections$learner_properties. predict_type (character(1)) Retrieves currently active predict type, e.g. \"response\".","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Surrogate Model — Surrogate","text":"Surrogate$new() Surrogate$update() Surrogate$reset() Surrogate$predict() Surrogate$format() Surrogate$print() Surrogate$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Surrogate Model — Surrogate","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$new(learner, archive, cols_x, cols_y, param_set)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model — Surrogate","text":"learner (learner) Arbitrary learner object depending subclass. archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. cols_y (character() | NULL) Column id's variables used targets. default, automatically inferred based archive. param_set (paradox::ParamSet) Parameter space description depending subclass.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Surrogate Model — Surrogate","text":"Train learner new data. Subclasses must implement private.update() private.update_async().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Surrogate Model — Surrogate","text":"Reset surrogate model. Subclasses must implement private$.reset().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"NULL","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-predict-","dir":"Reference","previous_headings":"","what":"Method predict()","title":"Surrogate Model — Surrogate","text":"Predict mean response standard error. Must implemented subclasses.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$predict(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model — Surrogate","text":"xdt (data.table::data.table()) New data. One row per observation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"Arbitrary prediction object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-format-","dir":"Reference","previous_headings":"","what":"Method format()","title":"Surrogate Model — Surrogate","text":"Helper print outputs.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$format()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-3","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"(character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Surrogate Model — Surrogate","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"returns-4","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model — Surrogate","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Surrogate Model — Surrogate","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model — Surrogate","text":"","code":"Surrogate$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/Surrogate.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model — Surrogate","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":null,"dir":"Reference","previous_headings":"","what":"Surrogate Model Containing a Single Learner — SurrogateLearner","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"Surrogate model containing single mlr3::LearnerRegr.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"assert_insample_perf logical(1) insample performance mlr3::LearnerRegr asserted updating surrogate? assertion fails (.e., insample performance based perf_measure meet perf_threshold), error thrown. Default FALSE. perf_measure mlr3::MeasureRegr Performance measure use assert insample performance mlr3::LearnerRegr. relevant assert_insample_perf = TRUE. Default mlr3::mlr_measures_regr.rsq. perf_threshold numeric(1) Threshold insample performance mlr3::LearnerRegr asserted . relevant assert_insample_perf = TRUE. Default 0. catch_errors logical(1) errors updating surrogate caught propagated loop_function can handle failed acquisition function optimization (result failed surrogate) appropriately , e.g., proposing randomly sampled point evaluation? Default TRUE. impute_method character(1) Method impute missing values case updating asynchronous bbotk::ArchiveAsync pending evaluations. Can \"mean\" use mean imputation \"random\" sample values uniformly random empirical minimum maximum. Default \"random\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"mlr3mbo::Surrogate -> SurrogateLearner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"print_id (character) Id used printing. n_learner (integer(1)) Returns number surrogate models. assert_insample_perf (numeric()) Asserts whether current insample performance meets performance threshold. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace(). feature_types (character()) Stores feature types surrogate can handle, e.g. \"logical\", \"numeric\", \"factor\". complete list candidate feature types, grouped task type, stored mlr_reflections$task_feature_types. properties (character()) Stores set properties/capabilities surrogate . complete list candidate properties, grouped task type, stored mlr_reflections$learner_properties. predict_type (character(1)) Retrieves currently active predict type, e.g. \"response\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"mlr3mbo::Surrogate$format() mlr3mbo::Surrogate$print() mlr3mbo::Surrogate$reset() mlr3mbo::Surrogate$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"SurrogateLearner$new() SurrogateLearner$predict() SurrogateLearner$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"SurrogateLearner$new(learner, archive = NULL, cols_x = NULL, col_y = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"learner (mlr3::LearnerRegr). archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. col_y (character(1) | NULL) Column id variable used target. default, automatically inferred based archive.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"method-predict-","dir":"Reference","previous_headings":"","what":"Method predict()","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"Predict mean response standard error.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"SurrogateLearner$predict(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"xdt (data.table::data.table()) New data. One row per observation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"data.table::data.table() columns mean se.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"SurrogateLearner$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearner.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Surrogate Model Containing a Single Learner — SurrogateLearner","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) xdt = generate_design_random(instance$search_space, n = 4)$data instance$eval_batch(xdt) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) surrogate$update() surrogate$learner$model } #> #> Call: #> DiceKriging::km(design = data, response = task$truth(), covtype = \"matern5_2\", #> nugget = 2.83305750865222e-07, optim.method = \"gen\", control = pv$control) #> #> Trend coeff.: #> Estimate #> (Intercept) 5.8590 #> #> Covar. type : matern5_2 #> Covar. coeff.: #> Estimate #> theta(x) 1.1710 #> #> Variance estimate: 21.95183 #> #> Nugget effect : 2.833058e-07 #>"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":null,"dir":"Reference","previous_headings":"","what":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"Surrogate model containing multiple mlr3::LearnerRegr. mlr3::LearnerRegr fit target variables indicated via cols_y. Note redundant mlr3::LearnerRegr must deep clones.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"assert_insample_perf logical(1) insample performance mlr3::LearnerRegr asserted updating surrogate? assertion fails (.e., insample performance based perf_measure meet perf_threshold), error thrown. Default FALSE. perf_measure List mlr3::MeasureRegr Performance measures use assert insample performance mlr3::LearnerRegr. relevant assert_insample_perf = TRUE. Default mlr3::mlr_measures_regr.rsq learner. perf_threshold List numeric(1) Thresholds insample performance mlr3::LearnerRegr asserted . relevant assert_insample_perf = TRUE. Default 0 learner. catch_errors logical(1) errors updating surrogate caught propagated loop_function can handle failed acquisition function optimization (result failed surrogate) appropriately , e.g., proposing randomly sampled point evaluation? Default TRUE. impute_method character(1) Method impute missing values case updating asynchronous bbotk::ArchiveAsync pending evaluations. Can \"mean\" use mean imputation \"random\" sample values uniformly random empirical minimum maximum. Default \"random\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"mlr3mbo::Surrogate -> SurrogateLearnerCollection","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"print_id (character) Id used printing. n_learner (integer(1)) Returns number surrogate models. assert_insample_perf (numeric()) Asserts whether current insample performance meets performance threshold. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace(). feature_types (character()) Stores feature types surrogate can handle, e.g. \"logical\", \"numeric\", \"factor\". complete list candidate feature types, grouped task type, stored mlr_reflections$task_feature_types. properties (character()) Stores set properties/capabilities surrogate . complete list candidate properties, grouped task type, stored mlr_reflections$learner_properties. predict_type (character(1)) Retrieves currently active predict type, e.g. \"response\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"mlr3mbo::Surrogate$format() mlr3mbo::Surrogate$print() mlr3mbo::Surrogate$reset() mlr3mbo::Surrogate$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"SurrogateLearnerCollection$new() SurrogateLearnerCollection$predict() SurrogateLearnerCollection$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"SurrogateLearnerCollection$new( learners, archive = NULL, cols_x = NULL, cols_y = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"learners (list mlr3::LearnerRegr). archive (bbotk::Archive | NULL)bbotk::Archive bbotk::OptimInstance. cols_x (character() | NULL) Column id's variables used features. default, automatically inferred based archive. cols_y (character() | NULL) Column id's variables used targets. default, automatically inferred based archive.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"method-predict-","dir":"Reference","previous_headings":"","what":"Method predict()","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"Predict mean response standard error. Returns named list data.tables. contains mean response standard error one col_y.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"SurrogateLearnerCollection$predict(xdt)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"xdt (data.table::data.table()) New data. One row per observation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"list data.table::data.table()s columns mean se.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"SurrogateLearnerCollection$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/SurrogateLearnerCollection.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Surrogate Model Containing Multiple Learners — SurrogateLearnerCollection","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\") & requireNamespace(\"ranger\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) xdt = generate_design_random(instance$search_space, n = 4)$data instance$eval_batch(xdt) learner1 = default_gp() learner2 = default_rf() surrogate = srlrn(list(learner1, learner2), archive = instance$archive) surrogate$update() surrogate$learner surrogate$learner[[\"y1\"]]$model surrogate$learner[[\"y2\"]]$model } #> Loading required namespace: ranger #> Ranger result #> #> Call: #> ranger::ranger(dependent.variable.name = task$target_names, data = task$data(), case.weights = task$weights$weight, keep.inbag = TRUE, num.threads = 1L, num.trees = 100L) #> #> Type: Regression #> Number of trees: 100 #> Sample size: 4 #> Number of independent variables: 1 #> Mtry: 1 #> Target node size: 5 #> Variable importance mode: none #> Splitrule: variance #> OOB prediction error (MSE): 4791.712 #> R squared (OOB): -0.404484"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Acquisition Function Construction — acqf","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"function complements mlr_acqfunctions functions spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"","code":"acqf(.key, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":".key (character(1)) Key passed respective dictionary retrieve object. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet, set public field. See mlr3misc::dictionary_sugar_get() details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"AcqFunction","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Acquisition Function Construction — acqf","text":"","code":"acqf(\"ei\") #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: epsilon ParamDbl 0 Inf Inf 0 0"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Acquisition Functions Construction — acqfs","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"function complements mlr_acqfunctions functions spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"","code":"acqfs(.keys, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":".keys (character()) Keys passed respective dictionary retrieve multiple objects. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet, set public field. See mlr3misc::dictionary_sugar_get() details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"List AcqFunctions","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqfs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Acquisition Functions Construction — acqfs","text":"","code":"acqfs(c(\"ei\", \"pi\", \"cb\")) #> $acq_ei #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: epsilon ParamDbl 0 Inf Inf 0 0 #> #> $acq_pi #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> #> $acq_cb #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: lambda ParamDbl 0 Inf Inf 2 2 #>"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"function allows construct AcqOptimizer spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"","code":"acqo(optimizer, terminator, acq_function = NULL, callbacks = NULL, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"optimizer (bbotk::OptimizerBatch)bbotk::OptimizerBatch used. terminator (bbotk::Terminator)bbotk::Terminator used. acq_function (NULL | AcqFunction)AcqFunction used. Can also NULL. callbacks (NULL | list mlr3misc::Callback) Callbacks used acquisition function optimization. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"AcqOptimizer","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/acqo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Acquisition Function Optimizer Construction — acqo","text":"","code":"library(bbotk) acqo(opt(\"random_search\"), trm(\"evals\"), catch_errors = FALSE) #> : (OptimizerBatchRandomSearch | TerminatorEvals) #> * Parameters: n_candidates=1, logging_level=warn, warmstart=FALSE, #> skip_already_evaluated=TRUE, catch_errors=FALSE"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Acquisition Function — default_acqfunction","title":"Default Acquisition Function — default_acqfunction","text":"Chooses default acquisition function, .e. criterion used propose future points. synchronous single-objective optimization, defaults mlr_acqfunctions_ei. synchronous multi-objective optimization, defaults mlr_acqfunctions_smsego. asynchronous single-objective optimization, defaults mlr_acqfunctions_stochastic_cb.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Acquisition Function — default_acqfunction","text":"","code":"default_acqfunction(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Acquisition Function — default_acqfunction","text":"instance (bbotk::OptimInstance). object inherits bbotk::OptimInstance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqfunction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Acquisition Function — default_acqfunction","text":"AcqFunction","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Acquisition Function Optimizer — default_acqoptimizer","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"Chooses default acquisition function optimizer. Defaults wrapping bbotk::OptimizerBatchRandomSearch allowing 10000 function evaluations (batch size 1000) via bbotk::TerminatorEvals.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"","code":"default_acqoptimizer(acq_function)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"acq_function (AcqFunction).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_acqoptimizer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Acquisition Function Optimizer — default_acqoptimizer","text":"AcqOptimizer","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Gaussian Process — default_gp","title":"Default Gaussian Process — default_gp","text":"helper function constructs default Gaussian Process mlr3::LearnerRegr example used default_surrogate. Constructs Kriging learner “\"regr.km\"” kernel “\"matern5_2\"”. noisy = FALSE (default) small nugget effect added nugget.stability = 10^-8 increase numerical stability hopefully prevent crashes DiceKriging. noisy = TRUE nugget effect estimated nugget.estim = TRUE. noisy = TRUE jitter set TRUE circumvent problem DiceKriging already trained input values produce exact trained output. general, instead default \"BFGS\" optimization method use rgenoud (\"gen\"), hybrid algorithm, combine global search based genetic algorithms local search based gradients. may improve model fit less frequently produce constant model prediction.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Gaussian Process — default_gp","text":"","code":"default_gp(noisy = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Gaussian Process — default_gp","text":"noisy (logical(1)) Whether learner used noisy objective function scenario. See .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Gaussian Process — default_gp","text":"mlr3::LearnerRegr","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Loop Function — default_loop_function","title":"Default Loop Function — default_loop_function","text":"Chooses default loop_function, .e. Bayesian Optimization flavor used optimization. single-objective optimization, defaults bayesopt_ego. multi-objective optimization, defaults bayesopt_smsego.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Loop Function — default_loop_function","text":"","code":"default_loop_function(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Loop Function — default_loop_function","text":"instance (bbotk::OptimInstance) object inherits bbotk::OptimInstance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_loop_function.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Loop Function — default_loop_function","text":"loop_function","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Result Assigner — default_result_assigner","title":"Default Result Assigner — default_result_assigner","text":"Chooses default result assigner. Defaults ResultAssignerArchive.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Result Assigner — default_result_assigner","text":"","code":"default_result_assigner(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Result Assigner — default_result_assigner","text":"instance (bbotk::OptimInstance) object inherits bbotk::OptimInstance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_result_assigner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Result Assigner — default_result_assigner","text":"ResultAssigner","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Random Forest — default_rf","title":"Default Random Forest — default_rf","text":"helper function constructs default random forest mlr3::LearnerRegr example used default_surrogate. Constructs ranger learner “\"regr.ranger\"” num.trees = 100, keep.inbag = TRUE se.method = \"jack\".","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Random Forest — default_rf","text":"","code":"default_rf(noisy = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Random Forest — default_rf","text":"noisy (logical(1)) Whether learner used noisy objective function scenario. Currently effect.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_rf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Random Forest — default_rf","text":"mlr3::LearnerRegr","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":null,"dir":"Reference","previous_headings":"","what":"Default Surrogate — default_surrogate","title":"Default Surrogate — default_surrogate","text":"helper function constructs default Surrogate based properties bbotk::OptimInstance. numeric-(including integers) parameter spaces without dependencies Gaussian Process constricted via default_gp(). mixed numeric-categorical parameter spaces, spaces conditional parameters random forest constructed via default_rf(). case, learners encapsulated using “\"evaluate\"”, fallback learner set, cases surrogate learner errors. Currently, following learner used fallback: lrn(\"regr.ranger\", num.trees = 10L, keep.inbag = TRUE, se.method = \"jack\"). additionally dependencies present parameter space, inactive conditional parameters represented missing NA values training design data. simply handle imputation method, added random forest, concretely use po(\"imputesample\") (logicals) po(\"imputeoor\") (anything else) package mlr3pipelines. Characters always encoded factors via po(\"colapply\"). range imputation makes sense tree-based methods usually hard beat, see Ding et al. (2010). case dependencies, following learner used fallback: lrn(\"regr.featureless\"). n_learner 1, learner wrapped SurrogateLearner. Otherwise, n_learner larger 1, multiple deep clones learner wrapped SurrogateLearnerCollection.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default Surrogate — default_surrogate","text":"","code":"default_surrogate( instance, learner = NULL, n_learner = NULL, force_random_forest = FALSE )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default Surrogate — default_surrogate","text":"instance (bbotk::OptimInstance) object inherits bbotk::OptimInstance. learner (NULL | mlr3::Learner). specified, learner used instead defaults described . n_learner (NULL | integer(1)). Number learners considered construction Surrogate. specified based number objectives stated instance. force_random_forest (logical(1)). TRUE, random forest constructed even parameter space numeric-.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default Surrogate — default_surrogate","text":"Surrogate","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/default_surrogate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Default Surrogate — default_surrogate","text":"Ding, Yufeng, Simonoff, S J (2010). “Investigation Missing Data Methods Classification Trees Applied Binary Response Data.” Journal Machine Learning Research, 11(1), 131–170.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/loop_function.html","id":null,"dir":"Reference","previous_headings":"","what":"Loop Functions for Bayesian Optimization — loop_function","title":"Loop Functions for Bayesian Optimization — loop_function","text":"Loop functions determine behavior Bayesian Optimization algorithm global level. overview readily available loop functions, see .data.table(mlr_loop_functions). general, loop function simply decorated member S3 class loop_function. Attributes must include: id (id loop function), label (brief description), instance (\"single-crit\" \"multi_crit\"), man (link manual page). example, see, e.g., bayesopt_ego.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mbo_defaults.html","id":null,"dir":"Reference","previous_headings":"","what":"Defaults for OptimizerMbo — mbo_defaults","title":"Defaults for OptimizerMbo — mbo_defaults","text":"following defaults set OptimizerMbo optimization respective fields set initialization. Optimization Loop: default_loop_function Surrogate: default_surrogate Acquisition Function: default_acqfunction Acqfun Optimizer: default_acqoptimizer Result Assigner: default_result_assigner","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr3mbo-package.html","id":null,"dir":"Reference","previous_headings":"","what":"mlr3mbo: Flexible Bayesian Optimization — mlr3mbo-package","title":"mlr3mbo: Flexible Bayesian Optimization — mlr3mbo-package","text":"modern flexible approach Bayesian Optimization / Model Based Optimization building 'bbotk' package. 'mlr3mbo' toolbox providing ready--use optimization algorithms well fundamental building blocks allowing straightforward implementation custom algorithms. Single- multi-objective optimization supported well mixed continuous, categorical conditional search spaces. Moreover, using 'mlr3mbo' hyperparameter optimization machine learning models within 'mlr3' ecosystem straightforward via 'mlr3tuning'. Examples ready--use optimization algorithms include Efficient Global Optimization Jones et al. (1998) doi:10.1023/:1008306431147 , ParEGO Knowles (2006) doi:10.1109/TEVC.2005.851274 SMS-EGO Ponweiser et al. (2008) doi:10.1007/978-3-540-87700-4_78 .","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr3mbo-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"mlr3mbo: Flexible Bayesian Optimization — mlr3mbo-package","text":"Maintainer: Lennart Schneider lennart.sch@web.de (ORCID) Authors: Jakob Richter jakob1richter@gmail.com (ORCID) Marc Becker marcbecker@posteo.de (ORCID) Michel Lang michellang@gmail.com (ORCID) Bernd Bischl bernd_bischl@gmx.net (ORCID) Florian Pfisterer pfistererf@googlemail.com (ORCID) Martin Binder mlr.developer@mb706.com Sebastian Fischer sebf.fischer@gmail.com (ORCID) contributors: Michael H. Buselli [copyright holder] Wessel Dankers [copyright holder] Carlos Fonseca [copyright holder] Manuel Lopez-Ibanez [copyright holder] Luis Paquete [copyright holder]","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of Acquisition Functions — mlr_acqfunctions","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"simple mlr3misc::Dictionary storing objects class AcqFunction. acquisition function associated help page, see mlr_acqfunctions_[id]. convenient way retrieve construct acquisition function, see acqf() acqfs().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"R6::R6Class object inheriting mlr3misc::Dictionary.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"See mlr3misc::Dictionary.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of Acquisition Functions — mlr_acqfunctions","text":"","code":"library(data.table) as.data.table(mlr_acqfunctions) #> Key: #> key label #> #> 1: aei Augmented Expected Improvement #> 2: cb Lower / Upper Confidence Bound #> 3: ehvi Expected Hypervolume Improvement #> 4: ehvigh Expected Hypervolume Improvement via GH Quadrature #> 5: ei Expected Improvement #> 6: eips Expected Improvement Per Second #> 7: mean Posterior Mean #> 8: multi Acquisition Function Wrapping Multiple Acquisition Functions #> 9: pi Probability Of Improvement #> 10: sd Posterior Standard Deviation #> 11: smsego SMS-EGO #> 12: stochastic_cb Stochastic Lower / Upper Confidence Bound #> 13: stochastic_ei Stochastic Expected Improvement #> man #> #> 1: mlr3mbo::mlr_acqfunctions_aei #> 2: mlr3mbo::mlr_acqfunctions_cb #> 3: mlr3mbo::mlr_acqfunctions_ehvi #> 4: mlr3mbo::mlr_acqfunctions_ehvigh #> 5: mlr3mbo::mlr_acqfunctions_ei #> 6: mlr3mbo::mlr_acqfunctions_eips #> 7: mlr3mbo::mlr_acqfunctions_mean #> 8: mlr3mbo::mlr_acqfunctions_multi #> 9: mlr3mbo::mlr_acqfunctions_pi #> 10: mlr3mbo::mlr_acqfunctions_sd #> 11: mlr3mbo::mlr_acqfunctions_smsego #> 12: mlr3mbo::mlr_acqfunctions_stochastic_cb #> 13: mlr3mbo::mlr_acqfunctions_stochastic_ei acqf(\"ei\") #> #> Domain: #> #> Empty. #> Codomain: #> #> Empty. #> Constants: #> #> id class lower upper nlevels default value #> #> 1: epsilon ParamDbl 0 Inf Inf 0 0"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Augmented Expected Improvement. Useful working noisy objectives. Currently works correctly \"regr.km\" surrogate model nugget.estim = TRUE given.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"aei\") acqf(\"aei\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"\"c\" (numeric(1)) Constant \\(c\\) used Formula (14) Huang (2012) reflect degree risk aversion. Defaults 1.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Huang D, Allen TT, Notz WI, Zheng N (2012). “Erratum : Global Optimization Stochastic Black-box Systems via Sequential Kriging Meta-Models.” Journal Global Optimization, 54(2), 431–431.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionAEI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"y_effective_best (numeric(1)) Best effective objective value observed far. case maximization, already includes necessary change sign. noise_var (numeric(1)) Estimate variance noise. corresponds nugget estimate using mlr3learners surrogate model.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"AcqFunctionAEI$new() AcqFunctionAEI$update() AcqFunctionAEI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"AcqFunctionAEI$new(surrogate = NULL, c = 1)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"surrogate (NULL | SurrogateLearner). c (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"Update acquisition function set y_effective_best noise_var.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"AcqFunctionAEI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"AcqFunctionAEI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_aei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Augmented Expected Improvement — mlr_acqfunctions_aei","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) set.seed(2906) fun = function(xs) { list(y = xs$x ^ 2 + rnorm(length(xs$x), mean = 0, sd = 1)) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain, properties = \"noisy\") instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = lrn(\"regr.km\", covtype = \"matern5_2\", optim.method = \"gen\", nugget.estim = TRUE, jitter = 1e-12, control = list(trace = FALSE)) surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"aei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_aei #> #> 1: 7.583607 #> 2: 7.583607 #> 3: 7.583607"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"Lower / Upper Confidence Bound.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"cb\") acqf(\"cb\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"\"lambda\" (numeric(1)) \\(\\lambda\\) value used confidence bound. Defaults 2.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionCB","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset() mlr3mbo::AcqFunction$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"AcqFunctionCB$new() AcqFunctionCB$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"","code":"AcqFunctionCB$new(surrogate = NULL, lambda = 2)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"surrogate (NULL | SurrogateLearner). lambda (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"","code":"AcqFunctionCB$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_cb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Confidence Bound — mlr_acqfunctions_cb","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"cb\", surrogate = surrogate, lambda = 3) acq_function$surrogate$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_cb #> #> 1: -55.38831 #> 2: -55.57158 #> 3: -49.63773"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Exact Expected Hypervolume Improvement. Calculates exact expected hypervolume improvement case two objectives. case optimizing two objective functions, AcqFunctionEHVIGH can used. See Emmerich et al. (2016) details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Emmerich, Michael, Yang, Kaifeng, Deutz, André, Wang, Hao, Fonseca, M. C (2016). “Multicriteria Generalization Bayesian Global Optimization.” Pardalos, M. P, Zhigljavsky, Anatoly, Žilinskas, Julius (eds.), Advances Stochastic Deterministic Global Optimization, 229–242. Springer International Publishing, Cham.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEHVI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"ys_front (matrix()) Approximated Pareto front. Sorted first objective. Signs corrected respect assuming minimization objectives. ref_point (numeric()) Reference point. Signs corrected respect assuming minimization objectives. ys_front_augmented (matrix()) Augmented approximated Pareto front. Sorted first objective. Signs corrected respect assuming minimization objectives.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"AcqFunctionEHVI$new() AcqFunctionEHVI$update() AcqFunctionEHVI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"AcqFunctionEHVI$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"surrogate (NULL | SurrogateLearnerCollection).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"Update acquisition function set ys_front ref_point.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"AcqFunctionEHVI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"AcqFunctionEHVI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Hypervolume Improvement — mlr_acqfunctions_ehvi","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) acq_function = acqf(\"ehvi\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ehvi #> #> 1: 206.4260 #> 2: 264.3261 #> 3: 376.3486"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Expected Hypervolume Improvement. Computed via Gauss-Hermite quadrature. case optimizing two objective functions AcqFunctionEHVI preferred.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"\"k\" (integer(1)) Number nodes per objective used numerical integration via Gauss-Hermite quadrature. Defaults 15. example, two objectives optimized, total number nodes therefore 225 per default. Changing value construction requires call $update() update $gh_data field. \"r\" (numeric(1)) Pruning rate 0 1 determines fraction nodes Gauss-Hermite quadrature rule ignored based weight value (nodes lowest weights ignored). Default 0.2. Changing value construction require call $update().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Rahat, Alma, Chugh, Tinkle, Fieldsend, Jonathan, Allmendinger, Richard, Miettinen, Kaisa (2022). “Efficient Approximation Expected Hypervolume Improvement using Gauss-Hermit Quadrature.” Rudolph, Günter, Kononova, V. , Aguirre, Hernán, Kerschke, Pascal, Ochoa, Gabriela, Tušar, Tea (eds.), Parallel Problem Solving Nature – PPSN XVII, 90–103.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEHVIGH","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"ys_front (matrix()) Approximated Pareto front. Signs corrected respect assuming minimization objectives. ref_point (numeric()) Reference point. Signs corrected respect assuming minimization objectives. hypervolume (numeric(1)). Current hypervolume approximated Pareto front respect reference point. gh_data (matrix()) Data required Gauss-Hermite quadrature rule form matrix dimension (k x 2). row corresponds one Gauss-Hermite node (column \"x\") corresponding weight (column \"w\"). Computed via fastGHQuad::gaussHermiteData. Nodes scaled factor sqrt(2) weights normalized sum one constraint.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"AcqFunctionEHVIGH$new() AcqFunctionEHVIGH$update() AcqFunctionEHVIGH$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"AcqFunctionEHVIGH$new(surrogate = NULL, k = 15L, r = 0.2)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"surrogate (NULL | SurrogateLearnerCollection). k (integer(1)). r (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"Update acquisition function set ys_front, ref_point, hypervolume gh_data.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"AcqFunctionEHVIGH$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"AcqFunctionEHVIGH$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ehvigh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Hypervolume Improvement via Gauss-Hermite Quadrature — mlr_acqfunctions_ehvigh","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) acq_function = acqf(\"ehvigh\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ehvigh #> #> 1: 136.3082 #> 2: 152.4999 #> 3: 166.7487"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Expected Improvement.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"ei\") acqf(\"ei\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"\"epsilon\" (numeric(1)) \\(\\epsilon\\) value used determine amount exploration. Higher values result importance improvements predicted posterior mean decreasing relative importance potential improvements regions high predictive uncertainty. Defaults 0 (standard Expected Improvement).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization Expensive Black-Box Functions.” Journal Global optimization, 13(4), 455–492.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"AcqFunctionEI$new() AcqFunctionEI$update() AcqFunctionEI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"AcqFunctionEI$new(surrogate = NULL, epsilon = 0)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"surrogate (NULL | SurrogateLearner). epsilon (numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"Update acquisition function set y_best.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"AcqFunctionEI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"AcqFunctionEI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_ei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Improvement — mlr_acqfunctions_ei","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"ei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ei #> #> 1: 4.092188 #> 2: 4.549039 #> 3: 5.037109"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Expected Improvement per Second. assumed calculations performed bbotk::OptimInstanceBatchSingleCrit. Additionally target values codomain minimized maximized, bbotk::Objective bbotk::OptimInstanceBatchSingleCrit return time values. column names target variable time variable must passed cols_y order (target, time) constructing SurrogateLearnerCollection used surrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"eips\") acqf(\"eips\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionEIPS","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"col_y (character(1)). col_time (character(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"AcqFunctionEIPS$new() AcqFunctionEIPS$update() AcqFunctionEIPS$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"AcqFunctionEIPS$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"surrogate (NULL | SurrogateLearnerCollection).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"Update acquisition function set y_best.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"AcqFunctionEIPS$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"AcqFunctionEIPS$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_eips.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Expected Improvement Per Second — mlr_acqfunctions_eips","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2, time = abs(xs$x)) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\"), time = p_dbl(tags = \"time\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) surrogate$cols_y = c(\"y\", \"time\") acq_function = acqf(\"eips\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_eips #> #> 1: 4.401246 #> 2: 4.864655 #> 3: 5.297142"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Mean — mlr_acqfunctions_mean","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"Posterior Mean.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"mean\") acqf(\"mean\")"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionMean","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset() mlr3mbo::AcqFunction$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"AcqFunctionMean$new() AcqFunctionMean$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"","code":"AcqFunctionMean$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"surrogate (NULL | SurrogateLearner).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"","code":"AcqFunctionMean$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Mean — mlr_acqfunctions_mean","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"mean\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_mean #> #> 1: 25.87163 #> 2: 23.23378 #> 3: 17.57580"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"Wrapping multiple AcqFunctions resulting multi-objective acquisition function composed individual ones. Note optimization direction wrapped acquisition function corrected maximization. acquisition function, Surrogate must used. acquisition functions passed construction already initialized surrogate, checked whether surrogate acquisition functions. acquisition functions initialized surrogate, surrogate passed construction lazy initialization used acquisition functions. optimization, AcqOptimizer can used AcqFunction, however, bbotk::OptimizerBatch wrapped within AcqOptimizer must support multi-objective optimization indicated via multi-crit property.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"multi\") acqf(\"multi\")"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionMulti","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"surrogate (Surrogate) Surrogate. acq_functions (list AcqFunction) Points list individual acquisition functions. acq_function_ids (character()) Points ids individual acquisition functions.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"AcqFunctionMulti$new() AcqFunctionMulti$update() AcqFunctionMulti$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"AcqFunctionMulti$new(acq_functions, surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"acq_functions (list AcqFunctions). surrogate (NULL | Surrogate).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"Update wrapped acquisition functions.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"AcqFunctionMulti$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"AcqFunctionMulti$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_multi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Wrapping Multiple Acquisition Functions — mlr_acqfunctions_multi","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"multi\", acq_functions = acqfs(c(\"ei\", \"pi\", \"cb\")), surrogate = surrogate ) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ei acq_pi acq_cb #> #> 1: 4.400964 0.2666736 28.30071 #> 2: 4.864368 0.2939486 29.30224 #> 3: 5.296907 0.3509359 27.23262"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Probability Improvement.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"pi\") acqf(\"pi\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Kushner, J. H (1964). “New Method Locating Maximum Point Arbitrary Multipeak Curve Presence Noise.” Journal Basic Engineering, 86(1), 97–106.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionPI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"AcqFunctionPI$new() AcqFunctionPI$update() AcqFunctionPI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"AcqFunctionPI$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"surrogate (NULL | SurrogateLearner).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"Update acquisition function set y_best.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"AcqFunctionPI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"AcqFunctionPI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_pi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Probability of Improvement — mlr_acqfunctions_pi","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"pi\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_pi #> #> 1: 0.2666813 #> 2: 0.2939562 #> 3: 0.3509427"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"Posterior Standard Deviation.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"sd\") acqf(\"sd\")"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionSD","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many() mlr3mbo::AcqFunction$reset() mlr3mbo::AcqFunction$update()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"AcqFunctionSD$new() AcqFunctionSD$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"","code":"AcqFunctionSD$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"surrogate (NULL | SurrogateLearner).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"","code":"AcqFunctionSD$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Standard Deviation — mlr_acqfunctions_sd","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"sd\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_sd #> #> 1: 27.08674 #> 2: 26.26854 #> 3: 22.40456"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"S-Metric Selection Evolutionary Multi-Objective Optimization Algorithm Acquisition Function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"\"lambda\" (numeric(1)) \\(\\lambda\\) value used confidence bound. Defaults 1. Based confidence = (1 - 2 * dnorm(lambda)) ^ m can calculate lambda given confidence level, see Ponweiser et al. (2008). \"epsilon\" (numeric(1)) \\(\\epsilon\\) used additive epsilon dominance. Can either single numeric value > 0 NULL (default). case NULL, epsilon vector maintained dynamically described Horn et al. (2015).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"acquisition function always also returns current epsilon values list column (acq_epsilon). values logged bbotk::ArchiveBatch bbotk::OptimInstanceBatch AcqOptimizer therefore also bbotk::Archive actual bbotk::OptimInstance optimized.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Ponweiser, Wolfgang, Wagner, Tobias, Biermann, Dirk, Vincze, Markus (2008). “Multiobjective Optimization Limited Budget Evaluations Using Model-Assisted S-Metric Selection.” Proceedings 10th International Conference Parallel Problem Solving Nature, 784–794. Horn, Daniel, Wagner, Tobias, Biermann, Dirk, Weihs, Claus, Bischl, Bernd (2015). “Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox Benchmark.” International Conference Evolutionary Multi-Criterion Optimization, 64–78.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionSmsEgo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"ys_front (matrix()) Approximated Pareto front. Signs corrected respect assuming minimization objectives. ref_point (numeric()) Reference point. Signs corrected respect assuming minimization objectives. epsilon (numeric()) Epsilon used additive epsilon dominance. progress (numeric(1)) Optimization progress (typically, number function evaluations left). Note requires bbotk::OptimInstanceBatch terminated via bbotk::TerminatorEvals.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"AcqFunctionSmsEgo$new() AcqFunctionSmsEgo$update() AcqFunctionSmsEgo$reset() AcqFunctionSmsEgo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$new(surrogate = NULL, lambda = 1, epsilon = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"surrogate (NULL | SurrogateLearnerCollection). lambda (numeric(1)). epsilon (NULL | numeric(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Update acquisition function set ys_front, ref_point epsilon.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"Reset acquisition function. Resets epsilon.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"AcqFunctionSmsEgo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_smsego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function SMS-EGO — mlr_acqfunctions_smsego","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(list(learner, learner$clone(deep = TRUE)), archive = instance$archive) acq_function = acqf(\"smsego\", surrogate = surrogate) acq_function$surrogate$update() acq_function$progress = 5 - 4 # n_evals = 5 and 4 points already evaluated acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_smsego acq_epsilon #> #> 1: -581.3330 0,0 #> 2: -730.5685 0,0 #> 3: -1132.3435 0,0"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Lower / Upper Confidence Bound lambda sampling decay. initial \\(\\lambda\\) drawn uniform distribution min_lambda max_lambda exponential distribution rate 1 / lambda. \\(\\lambda\\) updated update formula lambda * exp(-rate * (t %% period)), t number times acquisition function updated. acquisition function usually used within asynchronous optimizer, e.g., OptimizerAsyncMbo, can principle also used synchronous optimizers, e.g., OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"stochastic_cb\") acqf(\"stochastic_cb\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"\"lambda\" (numeric(1)) \\(\\lambda\\) value sampling exponential distribution. Defaults 1.96. \"min_lambda\" (numeric(1)) Minimum value \\(\\lambda\\)sampling uniform distribution. Defaults 0.01. \"max_lambda\" (numeric(1)) Maximum value \\(\\lambda\\) sampling uniform distribution. Defaults 10. \"distribution\" (character(1)) Distribution sample \\(\\lambda\\) . One c(\"uniform\", \"exponential\"). Defaults uniform. \"rate\" (numeric(1)) Rate exponential decay. Defaults 0 .e. decay. \"period\" (integer(1)) Period exponential decay. Defaults NULL, .e., decay period.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"acquisition function always also returns current (acq_lambda) original (acq_lambda_0) \\(\\lambda\\). values logged bbotk::ArchiveBatch bbotk::OptimInstanceBatch AcqOptimizer therefore also bbotk::Archive actual bbotk::OptimInstance optimized.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959. Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization Large Scale Hyperparameter Optimization.” 2023 IEEE 19th International Conference e-Science (e-Science), 1–10.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionStochasticCB","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"AcqFunctionStochasticCB$new() AcqFunctionStochasticCB$update() AcqFunctionStochasticCB$reset() AcqFunctionStochasticCB$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$new( surrogate = NULL, lambda = 1.96, min_lambda = 0.01, max_lambda = 10, distribution = \"uniform\", rate = 0, period = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"surrogate (NULL | SurrogateLearner). lambda (numeric(1)). min_lambda (numeric(1)). max_lambda (numeric(1)). distribution (character(1)). rate (numeric(1)). period (NULL | integer(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Update acquisition function. Samples decays lambda.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"Reset acquisition function. Resets private update counter .t used within epsilon decay.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"AcqFunctionStochasticCB$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_cb.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Stochastic Confidence Bound — mlr_acqfunctions_stochastic_cb","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"stochastic_cb\", surrogate = surrogate, lambda = 3) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_cb acq_lambda acq_lambda_0 #> #> 1: -165.5554 7.067187 7.067187 #> 2: -162.4109 7.067187 7.067187 #> 3: -140.7614 7.067187 7.067187"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":null,"dir":"Reference","previous_headings":"","what":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Expected Improvement epsilon decay. \\(\\epsilon\\) updated update formula epsilon * exp(-rate * (t %% period)) t number times acquisition function updated. acquisition function usually used within asynchronous optimizer, e.g., OptimizerAsyncMbo, can principle also used synchronous optimizers, e.g., OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"dictionary","dir":"Reference","previous_headings":"","what":"Dictionary","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"AcqFunction can instantiated via dictionary mlr_acqfunctions associated sugar function acqf():","code":"mlr_acqfunctions$get(\"stochastic_ei\") acqf(\"stochastic_ei\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"\"epsilon\" (numeric(1)) \\(\\epsilon\\) value used determine amount exploration. Higher values result importance improvements predicted posterior mean decreasing relative importance potential improvements regions high predictive uncertainty. Defaults 0.1. \"rate\" (numeric(1)) Defaults 0.05. \"period\" (integer(1)) Period exponential decay. Defaults NULL, .e., decay period.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"acquisition function always also returns current (acq_epsilon) original (acq_epsilon_0) \\(\\epsilon\\). values logged bbotk::ArchiveBatch bbotk::OptimInstanceBatch AcqOptimizer therefore also bbotk::Archive actual bbotk::OptimInstance optimized.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization Expensive Black-Box Functions.” Journal Global optimization, 13(4), 455–492.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"bbotk::Objective -> mlr3mbo::AcqFunction -> AcqFunctionStochasticEI","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"y_best (numeric(1)) Best objective function value observed far. case maximization, already includes necessary change sign.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"bbotk::Objective$eval() bbotk::Objective$format() bbotk::Objective$help() bbotk::Objective$print() mlr3mbo::AcqFunction$eval_dt() mlr3mbo::AcqFunction$eval_many()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"AcqFunctionStochasticEI$new() AcqFunctionStochasticEI$update() AcqFunctionStochasticEI$reset() AcqFunctionStochasticEI$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$new( surrogate = NULL, epsilon = 0.1, rate = 0.05, period = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"surrogate (NULL | SurrogateLearner). epsilon (numeric(1)). rate (numeric(1)). period (NULL | integer(1)).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-update-","dir":"Reference","previous_headings":"","what":"Method update()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Update acquisition function. Sets y_best best observed objective function value. Decays epsilon.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$update()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"Reset acquisition function. Resets private update counter .t used within epsilon decay.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"AcqFunctionStochasticEI$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_acqfunctions_stochastic_ei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Acquisition Function Stochastic Expected Improvement — mlr_acqfunctions_stochastic_ei","text":"","code":"if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) library(data.table) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) instance$eval_batch(data.table(x = c(-6, -5, 3, 9))) learner = default_gp() surrogate = srlrn(learner, archive = instance$archive) acq_function = acqf(\"stochastic_ei\", surrogate = surrogate) acq_function$surrogate$update() acq_function$update() acq_function$eval_dt(data.table(x = c(-1, 0, 1))) } #> acq_ei acq_epsilon acq_epsilon_0 #> #> 1: 4.374607 0.1 0.1 #> 2: 4.835292 0.1 0.1 #> 3: 5.262107 0.1 0.1"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of Loop Functions — mlr_loop_functions","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"simple mlr3misc::Dictionary storing objects class loop_function. loop function associated help page, see mlr_loop_functions_[id]. Retrieves object key key dictionary. Additional arguments must named passed constructor stored object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"R6::R6Class object inheriting mlr3misc::Dictionary.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"key (character(1)). ... () Passed constructor.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"Object corresponding key.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"See mlr3misc::Dictionary.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of Loop Functions — mlr_loop_functions","text":"","code":"library(data.table) as.data.table(mlr_loop_functions) #> Key: #> key label instance #> #> 1: bayesopt_ego Efficient Global Optimization single-crit #> 2: bayesopt_emo Multi-Objective EGO multi-crit #> 3: bayesopt_mpcl Multipoint Constant Liar single-crit #> 4: bayesopt_parego ParEGO multi-crit #> 5: bayesopt_smsego SMS-EGO multi-crit #> man #> #> 1: mlr3mbo::mlr_loop_functions_ego #> 2: mlr3mbo::mlr_loop_functions_emo #> 3: mlr3mbo::mlr_loop_functions_mpcl #> 4: mlr3mbo::mlr_loop_functions_parego #> 5: mlr3mbo::mlr_loop_functions_smsego"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"Loop function sequential single-objective Bayesian Optimization. Normally used inside OptimizerMbo. iteration initial design, surrogate acquisition function updated next candidate chosen based optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"","code":"bayesopt_ego( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"instance (bbotk::OptimInstanceBatchSingleCrit) bbotk::OptimInstanceBatchSingleCrit optimized. surrogate (Surrogate)Surrogate used surrogate. Typically SurrogateLearner. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchSingleCrit.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"Jones, R. D, Schonlau, Matthias, Welch, J. W (1998). “Efficient Global Optimization Expensive Black-Box Functions.” Journal Global optimization, 13(4), 455–492. Snoek, Jasper, Larochelle, Hugo, Adams, P R (2012). “Practical Bayesian Optimization Machine Learning Algorithms.” Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds.), Advances Neural Information Processing Systems, volume 25, 2951–2959.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_ego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Single-Objective Bayesian Optimization — mlr_loop_functions_ego","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) # expected improvement per second example fun = function(xs) { list(y = xs$x ^ 2, time = abs(xs$x)) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\"), time = p_dbl(tags = \"time\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance, n_learner = 2) surrogate$cols_y = c(\"y\", \"time\") optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acqf(\"eips\"), acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> x x_domain y #> #> 1: 1.55992 2.433351 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"Loop function sequential multi-objective Bayesian Optimization. Normally used inside OptimizerMbo. conceptual counterpart mlr_loop_functions_ego. iteration initial design, surrogate acquisition function updated next candidate chosen based optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"","code":"bayesopt_emo( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"instance (bbotk::OptimInstanceBatchMultiCrit) bbotk::OptimInstanceBatchMultiCrit optimized. surrogate (SurrogateLearnerCollection)SurrogateLearnerCollection used surrogate. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_emo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Multi-Objective Bayesian Optimization — mlr_loop_functions_emo","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"ehvi\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_emo, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> x x_domain y1 y2 #> #> 1: 0.7722247 0.5963309 1.5074323 #> 2: 1.0853905 1.1780725 0.8365106 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":null,"dir":"Reference","previous_headings":"","what":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"Loop function single-objective Bayesian Optimization via multipoint constant liar. Normally used inside OptimizerMbo. iteration initial design, surrogate acquisition function updated. acquisition function optimized, find candidate instead evaluating candidate, objective function value obtained applying liar function previously obtained objective function values. repeated q - 1 times obtain total q candidates evaluated single batch.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"","code":"bayesopt_mpcl( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, q = 2L, liar = mean, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"instance (bbotk::OptimInstanceBatchSingleCrit) bbotk::OptimInstanceBatchSingleCrit optimized. surrogate (Surrogate)Surrogate used surrogate. Typically SurrogateLearner. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. q (integer(1)) Batch size > 1. Default 2. liar (function) function accepting numeric vector input returning single numeric output. Default mean. sensible functions include min (max, depending optimization direction). random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchSingleCrit. make use parallel evaluations case `q > 1, objective function bbotk::OptimInstanceBatchSingleCrit must implemented accordingly.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"Ginsbourger, David, Le Riche, Rodolphe, Carraro, Laurent (2008). “Multi-Points Criterion Deterministic Parallel Global Optimization Based Gaussian Processes.” Wang, Jialei, Clark, C. S, Liu, Eric, Frazier, . P (2020). “Parallel Bayesian Global Optimization Expensive Functions.” Operations Research, 68(6), 1850–1865.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_mpcl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Single-Objective Bayesian Optimization via Multipoint Constant Liar — mlr_loop_functions_mpcl","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 7)) surrogate = default_surrogate(instance) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_mpcl, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer, args = list(q = 3)) optimizer$optimize(instance) } #> x x_domain y #> #> 1: 2.053603 4.217285 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":null,"dir":"Reference","previous_headings":"","what":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"Loop function multi-objective Bayesian Optimization via ParEGO. Normally used inside OptimizerMbo. iteration initial design, observed objective function values normalized q candidates obtained scalarizing values via augmented Tchebycheff function, updating surrogate respect scalarized values optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"","code":"bayesopt_parego( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, q = 1L, s = 100L, rho = 0.05, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"instance (bbotk::OptimInstanceBatchMultiCrit) bbotk::OptimInstanceBatchMultiCrit optimized. surrogate (SurrogateLearner)SurrogateLearner used surrogate. acq_function (AcqFunction)AcqFunction used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. q (integer(1)) Batch size, .e., number candidates obtained single batch. Default 1. s (integer(1)) \\(s\\) Equation 1 Knowles (2006). Determines total number possible random weight vectors. Default 100. rho (numeric(1)) \\(\\rho\\) Equation 2 Knowles (2006) scaling linear part augmented Tchebycheff function. Default 0.05 random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit. scalarizations objective function values stored y_scal column bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit. make use parallel evaluations case `q > 1, objective function bbotk::OptimInstanceBatchMultiCrit must implemented accordingly.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"Knowles, Joshua (2006). “ParEGO: Hybrid Algorithm -Line Landscape Approximation Expensive Multiobjective Optimization Problems.” IEEE Transactions Evolutionary Computation, 10(1), 50–66.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_parego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multi-Objective Bayesian Optimization via ParEGO — mlr_loop_functions_parego","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance, n_learner = 1) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_parego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> WARN [12:37:58.964] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this #> WARN [12:37:58.990] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. #> x x_domain y1 y2 #> #> 1: 0.7590663 0.5761817 1.539916 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"Loop function sequential multi-objective Bayesian Optimization via SMS-EGO. Normally used inside OptimizerMbo. iteration initial design, surrogate acquisition function (mlr_acqfunctions_smsego) updated next candidate chosen based optimizing acquisition function.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"","code":"bayesopt_smsego( instance, surrogate, acq_function, acq_optimizer, init_design_size = NULL, random_interleave_iter = 0L )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"instance (bbotk::OptimInstanceBatchMultiCrit) bbotk::OptimInstanceBatchMultiCrit optimized. surrogate (SurrogateLearnerCollection)SurrogateLearnerCollection used surrogate. acq_function (mlr_acqfunctions_smsego)mlr_acqfunctions_smsego used acquisition function. acq_optimizer (AcqOptimizer)AcqOptimizer used acquisition function optimizer. init_design_size (NULL | integer(1)) Size initial design. NULL bbotk::ArchiveBatch contains evaluations, 4 * d used d dimensionality search space. Points generated via Sobol sequence. random_interleave_iter (integer(1)) Every random_interleave_iter iteration (starting initial design), point sampled uniformly random evaluated (instead model based proposal). example, random_interleave_iter = 2, random interleaving performed second, fourth, sixth, ... iteration. Default 0, .e., random interleaving performed .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"invisible(instance) original instance modified -place returned invisible.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"acq_function$surrogate, even already populated, always overwritten surrogate. acq_optimizer$acq_function, even already populated, always overwritten acq_function. surrogate$archive, even already populated, always overwritten bbotk::ArchiveBatch bbotk::OptimInstanceBatchMultiCrit. Due iterative computation epsilon within mlr_acqfunctions_smsego, requires bbotk::Terminator bbotk::OptimInstanceBatchMultiCrit bbotk::TerminatorEvals.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"Beume N, Naujoks B, Emmerich M (2007). “SMS-EMOA: Multiobjective selection based dominated hypervolume.” European Journal Operational Research, 181(3), 1653–1669. Ponweiser, Wolfgang, Wagner, Tobias, Biermann, Dirk, Vincze, Markus (2008). “Multiobjective Optimization Limited Budget Evaluations Using Model-Assisted S-Metric Selection.” Proceedings 10th International Conference Parallel Problem Solving Nature, 784–794.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_loop_functions_smsego.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Multi-Objective Bayesian Optimization via SMS-EGO — mlr_loop_functions_smsego","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"smsego\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_smsego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> x x_domain y1 y2 #> #> 1: -0.08059442 0.006495461 4.328873142 #> 2: 1.95018192 3.803209514 0.002481841 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":null,"dir":"Reference","previous_headings":"","what":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"OptimizerADBO class implements Asynchronous Decentralized Bayesian Optimization (ADBO). ADBO variant Asynchronous Model Based Optimization (AMBO) uses AcqFunctionStochasticCB exponential lambda decay. Currently, single-objective optimization supported OptimizerADBO considered experimental feature API might subject changes.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"lambda parameter confidence bound acquisition function controls trade-exploration exploitation. large lambda value leads exploration, small lambda value leads exploitation. initial lambda value acquisition function used worker drawn exponential distribution rate 1 / lambda. ADBO can use periodic exponential decay reduce lambda periodically given time step t formula lambda * exp(-rate * (t %% period)). SurrogateLearner configured use random forest AcqOptimizer random search batch size 1000 budget 10000 evaluations.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"lambda numeric(1) Value used sampling lambda worker exponential distribution. rate numeric(1) Rate exponential decay. period integer(1) Period exponential decay. initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization Large Scale Hyperparameter Optimization.” 2023 IEEE 19th International Conference e-Science (e-Science), 1–10.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"bbotk::Optimizer -> bbotk::OptimizerAsync -> mlr3mbo::OptimizerAsyncMbo -> OptimizerADBO","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"bbotk::Optimizer$format() bbotk::Optimizer$help() mlr3mbo::OptimizerAsyncMbo$print() mlr3mbo::OptimizerAsyncMbo$reset()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"OptimizerADBO$new() OptimizerADBO$optimize() OptimizerADBO$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"OptimizerADBO$new()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"Performs optimization bbotk::OptimInstanceAsyncSingleCrit termination. single evaluations written bbotk::ArchiveAsync. result written instance object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"OptimizerADBO$optimize(inst)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"inst (bbotk::OptimInstanceAsyncSingleCrit).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"data.table::data.table()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"OptimizerADBO$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_adbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asynchronous Decentralized Bayesian Optimization — mlr_optimizers_adbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceAsyncSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) optimizer = opt(\"adbo\", design_size = 4, n_workers = 2) optimizer$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Loading required namespace: rush #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"OptimizerAsyncMbo class implements Asynchronous Model Based Optimization (AMBO). AMBO starts multiple sequential MBO runs different workers. worker communicate asynchronously shared archive relying rush package. optimizer follows modular layout surrogate model, acquisition function, acquisition optimizer can changed. SurrogateLearner impute missing values due pending evaluations. stochastic AcqFunction, e.g., AcqFunctionStochasticEI AcqFunctionStochasticCB used create varying versions acquisition function worker, promoting different exploration-exploitation trade-offs. AcqOptimizer class remains consistent one used synchronous MBO. contrast OptimizerMbo, loop_function can specified determines AMBO flavor OptimizerAsyncMbo simply relies surrogate update, acquisition function update acquisition function optimization step internal loop. Currently, single-objective optimization supported OptimizerAsyncMbo considered experimental feature API might subject changes. Note general SurrogateLearner updated one final time available data optimization process terminated. However, certain scenarios always possible meaningful. therefore recommended manually inspect SurrogateLearner optimization used, e.g., visualization purposes make sure properly updated available data. final update SurrogateLearner performed successfully, warning logged. specifying ResultAssigner, one can alter final result determined optimization, e.g., simply based evaluations logged archive ResultAssignerArchive based Surrogate via ResultAssignerSurrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"archive","dir":"Reference","previous_headings":"","what":"Archive","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"bbotk::ArchiveAsync holds following additional columns specific AMBO algorithms: acq_function$id (numeric(1)) value acquisition function. \".already_evaluated\" (logical(1)) Whether point already evaluated. Depends skip_already_evaluated parameter AcqOptimizer. bbotk::ArchiveAsync contain evaluations prior optimization, initial design needed. initial_design parameter specified data.table, data used. Otherwise, NULL, initial design size design_size generated based generate_design sampling function. See also parameters .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"bbotk::Optimizer -> bbotk::OptimizerAsync -> OptimizerAsyncMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"bbotk::Optimizer$format() bbotk::Optimizer$help()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"OptimizerAsyncMbo$new() OptimizerAsyncMbo$print() OptimizerAsyncMbo$reset() OptimizerAsyncMbo$optimize() OptimizerAsyncMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Creates new instance R6 class. surrogate NULL acq_function$surrogate field populated, SurrogateLearner used. Otherwise, default_surrogate(instance) used. acq_function NULL acq_optimizer$acq_function field populated, AcqFunction used (therefore $surrogate populated; see ). Otherwise default_acqfunction(instance) used. acq_optimizer NULL, default_acqoptimizer(instance) used. Even already initialized, surrogate$archive field always overwritten bbotk::ArchiveAsync current bbotk::OptimInstanceAsyncSingleCrit optimized. information default values surrogate, acq_function, acq_optimizer result_assigner, see ?mbo_defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$new( id = \"async_mbo\", surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, result_assigner = NULL, param_set = NULL, label = \"Asynchronous Model Based Optimization\", man = \"mlr3mbo::OptimizerAsyncMbo\" )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"id (character(1)) Identifier new instance. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_set (paradox::ParamSet) Set control parameters. label (character(1)) Label object. Can used tables, plot text output instead ID. man (character(1)) String format [pkg]::[topic] pointing manual page object. referenced help package can opened via method $help().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Reset optimizer. Sets following fields NULL: surrogate, acq_function, acq_optimizer,result_assigner Resets parameter values design_size design_function defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"Performs optimization bbotk::OptimInstanceAsyncSingleCrit termination. single evaluations written bbotk::ArchiveAsync. result written instance object.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$optimize(inst)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"inst (bbotk::OptimInstanceAsyncSingleCrit).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"data.table::data.table()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"OptimizerAsyncMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_async_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Asynchronous Model Based Optimization — mlr_optimizers_async_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(bbotk) library(paradox) library(mlr3learners) fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceAsyncSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) optimizer = opt(\"async_mbo\", design_size = 4, n_workers = 2) optimizer$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"Model Based Optimization — mlr_optimizers_mbo","title":"Model Based Optimization — mlr_optimizers_mbo","text":"OptimizerMbo class implements Model Based Optimization (MBO). implementation follows modular layout relying loop_function determining MBO flavor used, e.g., bayesopt_ego sequential single-objective Bayesian Optimization, Surrogate, AcqFunction, e.g., mlr_acqfunctions_ei Expected Improvement AcqOptimizer. MBO algorithms iterative optimization algorithms make use continuously updated surrogate model built objective function. optimizing comparably cheap evaluate acquisition function defined surrogate prediction, next candidate chosen evaluation. Detailed descriptions different MBO flavors provided documentation respective loop_function. Termination handled via bbotk::Terminator part bbotk::OptimInstanceBatch optimized. Note general Surrogate updated one final time available data optimization process terminated. However, certain scenarios always possible meaningful, e.g., using bayesopt_parego() multi-objective optimization uses surrogate relies scalarization objectives. therefore recommended manually inspect Surrogate optimization used, e.g., visualization purposes make sure properly updated available data. final update Surrogate performed successfully, warning logged. specifying ResultAssigner, one can alter final result determined optimization, e.g., simply based evaluations logged archive ResultAssignerArchive based Surrogate via ResultAssignerSurrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"archive","dir":"Reference","previous_headings":"","what":"Archive","title":"Model Based Optimization — mlr_optimizers_mbo","text":"bbotk::ArchiveBatch holds following additional columns specific MBO algorithms: acq_function$id (numeric(1)) value acquisition function. \".already_evaluated\" (logical(1)) Whether point already evaluated. Depends skip_already_evaluated parameter AcqOptimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"Model Based Optimization — mlr_optimizers_mbo","text":"bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Model Based Optimization — mlr_optimizers_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Model Based Optimization — mlr_optimizers_mbo","text":"bbotk::Optimizer$format() bbotk::Optimizer$help()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Model Based Optimization — mlr_optimizers_mbo","text":"OptimizerMbo$new() OptimizerMbo$print() OptimizerMbo$reset() OptimizerMbo$optimize() OptimizerMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Creates new instance R6 class. surrogate NULL acq_function$surrogate field populated, Surrogate used. Otherwise, default_surrogate(instance) used. acq_function NULL acq_optimizer$acq_function field populated, AcqFunction used (therefore $surrogate populated; see ). Otherwise default_acqfunction(instance) used. acq_optimizer NULL, default_acqoptimizer(instance) used. Even already initialized, surrogate$archive field always overwritten bbotk::ArchiveBatch current bbotk::OptimInstanceBatch optimized. information default values loop_function, surrogate, acq_function, acq_optimizer result_assigner, see ?mbo_defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$new( loop_function = NULL, surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, args = NULL, result_assigner = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Based Optimization — mlr_optimizers_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"Model Based Optimization — mlr_optimizers_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Reset optimizer. Sets following fields NULL: loop_function, surrogate, acq_function, acq_optimizer, args, result_assigner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-optimize-","dir":"Reference","previous_headings":"","what":"Method optimize()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"Performs optimization writes optimization result bbotk::OptimInstanceBatch. optimization result returned complete optimization path stored bbotk::ArchiveBatch bbotk::OptimInstanceBatch.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$optimize(inst)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Based Optimization — mlr_optimizers_mbo","text":"inst (bbotk::OptimInstanceBatch).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"Model Based Optimization — mlr_optimizers_mbo","text":"data.table::data.table.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Model Based Optimization — mlr_optimizers_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"OptimizerMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Model Based Optimization — mlr_optimizers_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_optimizers_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Model Based Optimization — mlr_optimizers_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(bbotk) library(paradox) library(mlr3learners) # single-objective EGO fun = function(xs) { list(y = xs$x ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchSingleCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) surrogate = default_surrogate(instance) acq_function = acqf(\"ei\") acq_optimizer = acqo( optimizer = opt(\"random_search\", batch_size = 100), terminator = trm(\"evals\", n_evals = 100)) optimizer = opt(\"mbo\", loop_function = bayesopt_ego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) # multi-objective ParEGO fun = function(xs) { list(y1 = xs$x^2, y2 = (xs$x - 2) ^ 2) } domain = ps(x = p_dbl(lower = -10, upper = 10)) codomain = ps(y1 = p_dbl(tags = \"minimize\"), y2 = p_dbl(tags = \"minimize\")) objective = ObjectiveRFun$new(fun = fun, domain = domain, codomain = codomain) instance = OptimInstanceBatchMultiCrit$new( objective = objective, terminator = trm(\"evals\", n_evals = 5)) optimizer = opt(\"mbo\", loop_function = bayesopt_parego, surrogate = surrogate, acq_function = acq_function, acq_optimizer = acq_optimizer) optimizer$optimize(instance) } #> WARN [12:38:01.985] [bbotk] Task 'surrogate_task' has missing values in column(s) 'y_scal', but learner 'regr.km' does not support this #> WARN [12:38:01.986] [bbotk] Could not update the surrogate a final time after the optimization process has terminated. #> x x_domain y1 y2 #> #> 1: 1.5279770 2.3347137 0.2228057 #> 2: -0.1195738 0.0142979 4.4925933 # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of Result Assigners — mlr_result_assigners","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"simple mlr3misc::Dictionary storing objects class ResultAssigner. acquisition function associated help page, see mlr_result_assigners_[id]. convenient way retrieve construct acquisition function, see ras().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"R6::R6Class object inheriting mlr3misc::Dictionary.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"See mlr3misc::Dictionary.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of Result Assigners — mlr_result_assigners","text":"","code":"library(data.table) as.data.table(mlr_result_assigners) #> Key: #> key label man #> #> 1: archive Archive mlr3mbo::mlr_result_assigners_archive #> 2: surrogate Mean Surrogate Prediction mlr3mbo::mlr_result_assigners_surrogate ras(\"archive\") #> "},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":null,"dir":"Reference","previous_headings":"","what":"Result Assigner Based on the Archive — mlr_result_assigners_archive","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"Result assigner chooses final point(s) based evaluations bbotk::Archive. mimics default behavior bbotk::Optimizer.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"mlr3mbo::ResultAssigner -> ResultAssignerArchive","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"mlr3mbo::ResultAssigner$format() mlr3mbo::ResultAssigner$print()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"ResultAssignerArchive$new() ResultAssignerArchive$assign_result() ResultAssignerArchive$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"ResultAssignerArchive$new()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"method-assign-result-","dir":"Reference","previous_headings":"","what":"Method assign_result()","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"Assigns result, .e., final point(s) instance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"ResultAssignerArchive$assign_result(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"instance (bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit |bbotk::OptimInstanceAsyncSingleCrit | bbotk::OptimInstanceAsyncMultiCrit) bbotk::OptimInstance final result assigned .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"ResultAssignerArchive$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_archive.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result Assigner Based on the Archive — mlr_result_assigners_archive","text":"","code":"result_assigner = ras(\"archive\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":null,"dir":"Reference","previous_headings":"","what":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"Result assigner chooses final point(s) based surrogate mean prediction evaluated points bbotk::Archive. especially useful case noisy objective functions. case operating bbotk::OptimInstanceBatchMultiCrit bbotk::OptimInstanceAsyncMultiCrit SurrogateLearnerCollection must use many learners objective functions.","code":""},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"super-class","dir":"Reference","previous_headings":"","what":"Super class","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"mlr3mbo::ResultAssigner -> ResultAssignerSurrogate","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"surrogate (Surrogate | NULL) surrogate. packages (character()) Set required packages. warning signaled least one packages installed, loaded (attached) later -demand via requireNamespace().","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"mlr3mbo::ResultAssigner$format() mlr3mbo::ResultAssigner$print()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"ResultAssignerSurrogate$new() ResultAssignerSurrogate$assign_result() ResultAssignerSurrogate$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"ResultAssignerSurrogate$new(surrogate = NULL)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"surrogate (Surrogate | NULL) surrogate used predict mean evaluated points.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"method-assign-result-","dir":"Reference","previous_headings":"","what":"Method assign_result()","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"Assigns result, .e., final point(s) instance. $surrogate NULL, default_surrogate(instance) used also assigned $surrogate.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"ResultAssignerSurrogate$assign_result(instance)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"instance (bbotk::OptimInstanceBatchSingleCrit | bbotk::OptimInstanceBatchMultiCrit |bbotk::OptimInstanceAsyncSingleCrit | bbotk::OptimInstanceAsyncMultiCrit) bbotk::OptimInstance final result assigned .","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"ResultAssignerSurrogate$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_result_assigners_surrogate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Result Assigner Based on a Surrogate Mean Prediction — mlr_result_assigners_surrogate","text":"","code":"result_assigner = ras(\"surrogate\")"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":null,"dir":"Reference","previous_headings":"","what":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"TunerADBO class implements Asynchronous Decentralized Bayesian Optimization (ADBO). ADBO variant Asynchronous Model Based Optimization (AMBO) uses AcqFunctionStochasticCB exponential lambda decay. minimal interface internally passing OptimizerAsyncMbo. additional information documentation see OptimizerAsyncMbo. Currently, single-objective optimization supported TunerADBO considered experimental feature API might subject changes.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Egelé, Romain, Guyon, Isabelle, Vishwanath, Venkatram, Balaprakash, Prasanna (2023). “Asynchronous Decentralized Bayesian Optimization Large Scale Hyperparameter Optimization.” 2023 IEEE 19th International Conference e-Science (e-Science), 1–10.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"mlr3tuning::Tuner -> mlr3tuning::TunerAsync -> mlr3tuning::TunerAsyncFromOptimizerAsync -> TunerADBO","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"mlr3tuning::Tuner$format() mlr3tuning::Tuner$help() mlr3tuning::TunerAsyncFromOptimizerAsync$optimize()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"TunerADBO$new() TunerADBO$print() TunerADBO$reset() TunerADBO$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Creates new instance R6 class.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$new()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"Reset tuner. Sets following fields NULL: surrogate, acq_function, acq_optimizer, result_assigner Resets parameter values design_size design_function defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"TunerADBO$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_adbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TunerAsync using Asynchronous Decentralized Bayesian Optimization — mlr_tuners_adbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(mlr3) library(mlr3tuning) # single-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measure = msr(\"classif.acc\") instance = TuningInstanceAsyncSingleCrit$new( task = task, learner = learner, resampling = resampling, measure = measure, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) tnr(\"adbo\", design_size = 4, n_workers = 2)$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"TunerAsyncMbo class implements Asynchronous Model Based Optimization (AMBO). minimal interface internally passing OptimizerAsyncMbo. additional information documentation see OptimizerAsyncMbo. Currently, single-objective optimization supported TunerAsyncMbo considered experimental feature API might subject changes.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"parameters","dir":"Reference","previous_headings":"","what":"Parameters","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"initial_design data.table::data.table() Initial design optimization. NULL, design size design_size generated specified design_function. Default NULL. design_size integer(1) Size initial design generated. Default 100. design_function character(1) Sampling function generate initial design. Can random paradox::generate_design_random, lhs paradox::generate_design_lhs, sobol paradox::generate_design_sobol. Default sobol. n_workers integer(1) Number parallel workers. NULL, rush workers specified via rush::rush_plan() used. Default NULL.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"mlr3tuning::Tuner -> mlr3tuning::TunerAsync -> mlr3tuning::TunerAsyncFromOptimizerAsync -> TunerAsyncMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"mlr3tuning::Tuner$format() mlr3tuning::Tuner$help() mlr3tuning::TunerAsyncFromOptimizerAsync$optimize()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"TunerAsyncMbo$new() TunerAsyncMbo$print() TunerAsyncMbo$reset() TunerAsyncMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"Creates new instance R6 class. information default values surrogate, acq_function, acq_optimizer, result_assigner, see ?mbo_defaults. Note parameters simply passed OptimizerAsyncMbo respective fields simply (settable) active bindings fields OptimizerAsyncMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$new( surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, param_set = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. param_set (paradox::ParamSet) Set control parameters.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"Reset tuner. Sets following fields NULL: surrogate, acq_function, acq_optimizer, result_assigner Resets parameter values design_size design_function defaults.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"TunerAsyncMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_async_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TunerAsync using Asynchronous Model Based Optimization — mlr_tuners_async_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"rush\") & requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { if (redis_available()) { library(mlr3) library(mlr3tuning) # single-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measure = msr(\"classif.acc\") instance = TuningInstanceAsyncSingleCrit$new( task = task, learner = learner, resampling = resampling, measure = measure, terminator = trm(\"evals\", n_evals = 10)) rush::rush_plan(n_workers=2) tnr(\"async_mbo\", design_size = 4, n_workers = 2)$optimize(instance) } else { message(\"Redis server is not available.\\nPlease set up Redis prior to running the example.\") } } #> Redis server is not available. #> Please set up Redis prior to running the example. # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":null,"dir":"Reference","previous_headings":"","what":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"TunerMbo class implements Model Based Optimization (MBO). minimal interface internally passing OptimizerMbo. additional information documentation see OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"super-classes","dir":"Reference","previous_headings":"","what":"Super classes","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerMbo","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"active-bindings","dir":"Reference","previous_headings":"","what":"Active bindings","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner. param_classes (character()) Supported parameter classes optimizer can optimize. Determined based surrogate acq_optimizer. corresponds values given paradox::ParamSet's $class field. properties (character()) Set properties optimizer. Must subset bbotk_reflections$optimizer_properties. MBO principle flexible default assume optimizer properties. fully initialized, properties determined based loop, e.g., loop_function, surrogate. packages (character()) Set required packages. warning signaled prior optimization least one packages installed, loaded (attached) later -demand via requireNamespace(). Required packages determined based acq_function, surrogate acq_optimizer.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"methods","dir":"Reference","previous_headings":"","what":"Methods","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"mlr3tuning::Tuner$format() mlr3tuning::Tuner$help() mlr3tuning::TunerBatchFromOptimizerBatch$optimize()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"TunerMbo$new() TunerMbo$print() TunerMbo$reset() TunerMbo$clone()","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"Creates new instance R6 class. information default values loop_function, surrogate, acq_function, acq_optimizer, result_assigner, see ?mbo_defaults. Note parameters simply passed OptimizerMbo respective fields simply (settable) active bindings fields OptimizerMbo.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$new( loop_function = NULL, surrogate = NULL, acq_function = NULL, acq_optimizer = NULL, args = NULL, result_assigner = NULL )"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"loop_function (loop_function | NULL) Loop function determining MBO flavor. surrogate (Surrogate | NULL) surrogate. acq_function (AcqFunction | NULL) acquisition function. acq_optimizer (AcqOptimizer | NULL) acquisition function optimizer. args (named list()) arguments passed loop_function. example, random_interleave_iter. result_assigner (ResultAssigner | NULL) result assigner.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-print-","dir":"Reference","previous_headings":"","what":"Method print()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"Print method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$print()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"(character()).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-reset-","dir":"Reference","previous_headings":"","what":"Method reset()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"Reset tuner. Sets following fields NULL: loop_function, surrogate, acq_function, acq_optimizer, args, result_assigner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$reset()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"objects class cloneable method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"TunerMbo$clone(deep = FALSE)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"deep Whether make deep clone.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/mlr_tuners_mbo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TunerBatch using Model Based Optimization — mlr_tuners_mbo","text":"","code":"# \\donttest{ if (requireNamespace(\"mlr3learners\") & requireNamespace(\"DiceKriging\") & requireNamespace(\"rgenoud\")) { library(mlr3) library(mlr3tuning) # single-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measure = msr(\"classif.acc\") instance = TuningInstanceBatchSingleCrit$new( task = task, learner = learner, resampling = resampling, measure = measure, terminator = trm(\"evals\", n_evals = 5)) tnr(\"mbo\")$optimize(instance) # multi-objective task = tsk(\"wine\") learner = lrn(\"classif.rpart\", cp = to_tune(lower = 1e-4, upper = 1, logscale = TRUE)) resampling = rsmp(\"cv\", folds = 3) measures = msrs(c(\"classif.acc\", \"selected_features\")) instance = TuningInstanceBatchMultiCrit$new( task = task, learner = learner, resampling = resampling, measures = measures, terminator = trm(\"evals\", n_evals = 5), store_models = TRUE) # required due to selected features tnr(\"mbo\")$optimize(instance) } #> Error: Measure 'selected_features' requires the trained model # }"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Result Assigner Construction — ras","title":"Syntactic Sugar Result Assigner Construction — ras","text":"function complements mlr_result_assigners functions spirit mlr_sugar mlr3.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Result Assigner Construction — ras","text":"","code":"ras(.key, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Result Assigner Construction — ras","text":".key (character(1)) Key passed respective dictionary retrieve object. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet, set public field. See mlr3misc::dictionary_sugar_get() details.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Result Assigner Construction — ras","text":"ResultAssigner","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/ras.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Result Assigner Construction — ras","text":"","code":"ras(\"archive\") #> "},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if Redis Server is Available — redis_available","title":"Check if Redis Server is Available — redis_available","text":"Attempts establish connection Redis server using redux package sends PING command. Returns TRUE server available responds appropriately, FALSE otherwise.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if Redis Server is Available — redis_available","text":"","code":"redis_available()"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if Redis Server is Available — redis_available","text":"(logical(1))","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/redis_available.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if Redis Server is Available — redis_available","text":"","code":"if (redis_available()) { # Proceed with code that requires Redis message(\"Redis server is available.\") } else { message(\"Redis server is not available.\") } #> Redis server is not available."},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":null,"dir":"Reference","previous_headings":"","what":"Syntactic Sugar Surrogate Construction — srlrn","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"function allows construct SurrogateLearner SurrogateLearnerCollection spirit mlr_sugar mlr3. archive references one target variable cols_y contains one target variable single learner specified, learner replicated many times needed build SurrogateLearnerCollection.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"","code":"srlrn(learner, archive = NULL, cols_x = NULL, cols_y = NULL, ...)"},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"learner (mlr3::LearnerRegr | List mlr3::LearnerRegr)mlr3::LearnerRegr used within SurrogateLearner list mlr3::LearnerRegr used within SurrogateLearnerCollection. archive (NULL | bbotk::Archive)bbotk::Archive bbotk::OptimInstance used. Can also NULL. cols_x (NULL | character()) Column ids bbotk::Archive used features. Can also NULL case automatically inferred based archive. cols_y (NULL | character()) Column id(s) bbotk::Archive used target. list mlr3::LearnerRegr provided learner argument cols_y specified well, many column names learners must provided. Can also NULL case automatically inferred based archive. ... (named list()) Named arguments passed constructor, set parameters paradox::ParamSet.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"SurrogateLearner | SurrogateLearnerCollection","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/reference/srlrn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Syntactic Sugar Surrogate Construction — srlrn","text":"","code":"library(mlr3) srlrn(lrn(\"regr.featureless\"), catch_errors = FALSE) #> : LearnerRegrFeatureless #> * Parameters: assert_insample_perf=FALSE, catch_errors=FALSE, #> impute_method=random srlrn(list(lrn(\"regr.featureless\"), lrn(\"regr.featureless\"))) #> : (LearnerRegrFeatureless | LearnerRegrFeatureless) #> * Parameters: assert_insample_perf=FALSE, catch_errors=TRUE, #> impute_method=random"},{"path":[]},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-028","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.8","title":"mlr3mbo 0.2.8","text":"CRAN release: 2024-11-21 docs: gracefully exit examples OptimizerAsyncMbo, OptimizerADBO, TunerAsyncMbo, TunerADBO Redis available. test: skip tests involving asynchronous logic Redis available.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-027","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.7","title":"mlr3mbo 0.2.7","text":"CRAN release: 2024-11-15 refactor: refactored SurrogateLearner SurrogateLearnerCollection allow updating asynchronous Archive. feat: added experimental OptimizerAsyncMbo, OptimizerADBO, TunerAsyncMbo, TunerADBO allow asynchronous optimization. feat: added AcqFunctionStochasticCB AcqFunctionStochasticEI useful asynchronous optimization. docs: minor changes highlight differences batch asynchronous objects related asynchronous support. refactor: AcqFunctions AcqOptimizer gained reset() method.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-026","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.6","title":"mlr3mbo 0.2.6","text":"CRAN release: 2024-10-16 refactor: Extract internal tuned values instance.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-025","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.5","title":"mlr3mbo 0.2.5","text":"CRAN release: 2024-09-24 docs: Move vignette mlr3book. feat: Add AcqFunctionMulti can wrap multiple acquisition functions resulting multi-objective acquisition function problem. feat: Support callbacks AcqOptimizer. feat: Allow AcqFunctionEI adjusted epsilon strengthen exploration.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-024","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.4","title":"mlr3mbo 0.2.4","text":"CRAN release: 2024-07-06 fix: Improve runtime AcqOptimizer setting check_values = FALSE.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-023","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.3","title":"mlr3mbo 0.2.3","text":"CRAN release: 2024-07-01 compatibility: Work new bbotk mlr3tuning version 1.0.0.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-022","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.2","title":"mlr3mbo 0.2.2","text":"CRAN release: 2024-03-01 refactor: compatibility upcoming paradox upgrade. feat: OptimizerMbo TunerMbo now update Surrogate final time optimization process finished ensure Surrogate correctly reflects state trained data seen optimization. fix: AcqFunction domain construction now respects Surrogate cols_x field. feat: support one candidate point result acquisition function optimization even non-batch acquisition functions. feat: added default_gp default_rf helpers allow construction default Gaussian Process random forest example used within default_surrogate. refactor: changed Gaussian Process random forest defaults (default_gp default_rf therefore also default_surrogate). Gaussian Process now uses \"matern5_2\" kernel. Random forest now uses 100 trees. number trees used fallback random forest reduced 10.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-021","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.2.1","title":"mlr3mbo 0.2.1","text":"CRAN release: 2023-06-05 docs: updated references vignette. refactor: minor clean internal structure loop functions. perf: default initial design constructed based Sobol sequence loop functions. refactor: longer depend mlr3tuning import instead. refactor: srlrn sugar function now can construct SurrogateLearner SurrogateLearnerCollection; dropped srlrnc. feat: added AcqFunctionSD, AcqFunctionEHVI AcqFunctionEHVIGH, introduced bayesopt_emo loop function. feat: AcqFunctions now include $packages field stating required packages checked whether namespace can loaded prior optimization. fix: fixed bug fix_xdt_missing() helper function. BREAKING CHANGE: renaming default_loopfun -> default_loop_function, default_acqfun -> default_acqfunction, default_acqopt -> default_acqoptimizer. BREAKING CHANGE: result_functions now replaced ResultAssigners. BREAKING CHANGE: renamed $model field Surrogate classes $learner. BREAKING CHANGE: Surrogate AcquisitionFunction classes fields *_cols renamed cols_* (e.g., x_cols cols_x).","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-012","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.1.2","title":"mlr3mbo 0.1.2","text":"CRAN release: 2023-03-02 refactor: adapt mlr3tuning 0.18.0. feat: Acquisition functions now assert whether surrogates match required predict type. fix: Unloading mlr3mbo removes optimizers tuners dictionaries. docs: faster examples. feat: characters surrogate regression tasks longer automatically converted factors. default_surrogate now respects gained appropriate pipeline step. feat: AcqFunctionAEI added. docs: fix docs, README bibentries.","code":""},{"path":"https://mlr3mbo.mlr-org.com/dev/news/index.html","id":"mlr3mbo-011","dir":"Changelog","previous_headings":"","what":"mlr3mbo 0.1.1","title":"mlr3mbo 0.1.1","text":"CRAN release: 2022-11-18 Initial upload CRAN.","code":""}]