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feat: add mbo defaults (#192)
* push latest so_config attic * new so_config * fix: always add dropped columns to xdt during surrogate predict * new so_config * use current bbotk main branch * LearnerRegrRangerCustom * yahpo experiments * fix init_design_size for random_interleave, acqopt logging and warmstart * tweaks in acq opt and warmstart * some more infill optimization tweaks for so config * .. * new ac setup * .. * .. * .. * .. * .. * .. * .. * .. * .. * play around with log scaling * chore: AcqFunctionCB cleanup * merge acqf_ttei from acqf_ttei branch for now * .. * prepare new so_config * .. * new cd version * some so config try-outs * mies baseline * new eval scheme for so_config * .. * .. * .. * .. * .. * .. * .. * .. * some changes to char to fct handling, ... * .. * ... * .. * .. * add learner * fix impl errors, add docs * update * add to dict? * .. * reiterate lfbo * rerun docs * .. * manually require setting the lfbo direction * .. * .. * .. * .. * .. * .. * fix: fix_xdt_missing for logical NA * .. * .. * fix: remove reqired from parameters with default * fix: bbotk update * fix: optimizerchain * update * refactor: local search with new paradox * fix: cols_y * fix: remove browser() * chore: browser * fix: local search * fix: minimize * refactor: remove chain and local search * feat: add epsilon to log ei * .. * feat: rework LearnerRegrRangerMbo * .. * fix: make log ei more robost * se >= 1e-8 for LearnerRegrRangerMbo and law of total variance * .. * perf: speed up surrogate predictions * draft SurrogateGP * typo * .. * .. * .. * .. * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * add libcmaesr * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * fix direction * feat: add mbo defaults * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... * ... --------- Co-authored-by: Lennart Schneider <[email protected]> Co-authored-by: pfistfl <[email protected]>
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‎.lintr‎

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linters: linters_with_defaults(
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# lintr defaults: https://github.com/jimhester/lintr#available-linters
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# lintr defaults: https://lintr.r-lib.org/reference/default_linters.html
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# the following setup changes/removes certain linters
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assignment_linter = NULL, # do not force using <- for assignments
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object_name_linter = object_name_linter(c("snake_case", "CamelCase")), # only allow snake case and camel case object names
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cyclocomp_linter = NULL, # do not check function complexity
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object_name_linter(c("snake_case", "CamelCase")), # only allow snake case and camel case object names
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commented_code_linter = NULL, # allow code in comments
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line_length_linter = line_length_linter(120),
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indentation_linter(indent = 2L, hanging_indent_style = "never")
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)
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line_length_linter(200L),
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object_length_linter(40L),
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undesirable_function_linter(fun = c(
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# base messaging
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cat = "use catf()",
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stop = "use stopf()",
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warning = "use warningf()",
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message = "use messagef()",
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# perf
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ifelse = "use fifelse()",
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rank = "use frank()"
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))
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)

‎DESCRIPTION‎

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@@ -39,14 +39,14 @@ License: LGPL-3
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URL: https://mlr3mbo.mlr-org.com, https://github.com/mlr-org/mlr3mbo
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BugReports: https://github.com/mlr-org/mlr3mbo/issues
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Depends:
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mlr3 (>= 1.2.0),
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mlr3tuning (>= 1.4.0),
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R (>= 3.1.0)
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Imports:
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bbotk (>= 1.6.0),
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bbotk (>= 1.8.0),
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checkmate (>= 2.0.0),
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data.table,
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lgr (>= 0.3.4),
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mlr3 (>= 0.23.0),
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mlr3misc (>= 0.15.1),
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paradox (>= 1.0.1),
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spacefillr,
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emoa,
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fastGHQuad,
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lhs,
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mlr3learners (>= 0.12.0),
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mirai,
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mlr3learners (>= 0.7.0),
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mlr3pipelines (>= 0.5.2),
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nloptr,
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ranger,
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redux,
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rush,
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stringi,
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testthat (>= 3.0.0)
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testthat (>= 3.0.0),
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ByteCompile: no
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Encoding: UTF-8
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Config/testthat/edition: 3
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Config/testthat/parallel: false
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NeedsCompilation: yes
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Roxygen: list(markdown = TRUE, r6 = TRUE)
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RoxygenNote: 7.3.2
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RoxygenNote: 7.3.3
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Collate:
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'mlr_acqfunctions.R'
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'AcqFunction.R'
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'AcqFunctionStochasticCB.R'
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'AcqFunctionStochasticEI.R'
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'AcqOptimizer.R'
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'mlr_acqoptimizers.R'
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'AcqOptimizerDirect.R'
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'AcqOptimizerLbfgsb.R'
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'AcqOptimizerLocalSearch.R'
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'AcqOptimzerRandomSearch.R'
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'mlr_input_trafos.R'
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'InputTrafo.R'
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'InputTrafoUnitcube.R'

‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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S3method(as.data.table,DictionaryAcqFunction)
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S3method(as.data.table,DictionaryAcqOptimizer)
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S3method(as.data.table,DictionaryInputTrafo)
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S3method(as.data.table,DictionaryLoopFunction)
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S3method(as.data.table,DictionaryOutputTrafo)
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export(AcqFunctionStochasticCB)
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export(AcqFunctionStochasticEI)
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export(AcqOptimizer)
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export(AcqOptimizerDirect)
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export(AcqOptimizerLbfgsb)
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export(AcqOptimizerLocalSearch)
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export(AcqOptimizerRandomSearch)
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export(InputTrafo)
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export(InputTrafoUnitcube)
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export(OptimizerADBO)
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export(default_surrogate)
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export(it)
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export(mlr_acqfunctions)
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export(mlr_acqoptimizers)
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export(mlr_input_trafos)
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export(mlr_loop_functions)
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export(mlr_output_trafos)

‎NEWS.md‎

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# mlr3mbo (development version)
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* feat: Added `mlr_acqoptimizers` dictionary with predefined acquisition function optimizers.
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* perf: Added `AcqOptimizerDirect`, `AcqOptimizerLbfgsb`, `AcqOptimizerLocalSearch`, and `AcqOptimizerRandomSearch`.
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* feat: `default_*` helpers return new empirical based default values.
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# mlr3mbo 0.3.3
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* compatibility: bbotk 1.7.0

‎R/AcqOptimizerDirect.R‎

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#' @title Direct Acquisition Function Optimizer
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#'
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#' @include AcqOptimizer.R mlr_acqoptimizers.R
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#'
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#' @description
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#' Direct acquisition function optimizer.
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#' Calls `nloptr()` from \CRANpkg{nloptr}.
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#' In its default setting, the algorithm restarts `5 * D` times and runs at most for `100 * D^2` function evaluations, where `D` is the dimension of the search space.
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#' Each run stops when the relative tolerance of the parameters is less than `10^-4`.
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#' The first iteration starts with the best point in the archive and the next iterations start from a random point.
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#'
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#' @section Parameters:
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#' \describe{
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#' \item{`restart_strategy`}{`character(1)`\cr
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#' Restart strategy.
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#' Can be `"none"` or `"random"`.
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#' Default is `"none"`.
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#' }
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#' \item{`max_restarts`}{`integer(1)`\cr
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#' Maximum number of restarts.
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#' Default is `5 * D` (Default).}
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#' }
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#'
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#' @note
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#' If the restart strategy is `"none"`, the optimizer starts with the best point in the archive.
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#' The optimization stops when one of the stopping criteria is met.
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#'
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#' If `restart_strategy` is `"random"`, the optimizer runs at least for `maxeval` iterations.
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#' The first iteration starts with the best point in the archive and stops when one of the stopping criteria is met.
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#' The next iterations start from a random point.
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#'
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#' @section Termination Parameters:
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#' The following termination parameters can be used.
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#'
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#' \describe{
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#' \item{`stopval`}{`numeric(1)`\cr
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#' Stop value.
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#' Deactivate with `-Inf` (Default).}
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#' \item{`maxeval`}{`integer(1)`\cr
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#' Maximum number of evaluations.
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#' Default is `100 * D^2`, where `D` is the dimension of the search space.
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#' Deactivate with `-1L`.}
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#' \item{`xtol_rel`}{`numeric(1)`\cr
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#' Relative tolerance of the parameters.
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#' Default is `10^-4`.
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#' Deactivate with `-1`.}
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#' \item{`xtol_abs`}{`numeric(1)`\cr
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#' Absolute tolerance of the parameters.
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#' Deactivate with `-1` (Default).}
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#' \item{`ftol_rel`}{`numeric(1)`\cr
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#' Relative tolerance of the objective function.
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#' Deactivate with `-1`. (Default).}
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#' \item{`ftol_abs`}{`numeric(1)`\cr
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#' Absolute tolerance of the objective function.
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#' Deactivate with `-1` (Default).}
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#' }
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#'
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#' @export
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#' @examples
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#' if (requireNamespace("nloptr")) {
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#' acqo("direct")
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#' }
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AcqOptimizerDirect = R6Class("AcqOptimizerDirect",
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inherit = AcqOptimizer,
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public = list(
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#' @field state (`list()`)\cr
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#' List of [nloptr::nloptr()] results.
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state = NULL,
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#' @description
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#' Creates a new instance of this [R6][R6::R6Class] class.
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#'
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#' @param acq_function (`NULL` | [AcqFunction]).
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initialize = function(acq_function = NULL) {
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self$acq_function = assert_r6(acq_function, "AcqFunction", null.ok = TRUE)
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param_set = ps(
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maxeval = p_int(),
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stopval = p_dbl(default = -Inf, lower = -Inf, upper = Inf),
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xtol_rel = p_dbl(default = 1e-04, lower = 0, upper = Inf, special_vals = list(-1L)),
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xtol_abs = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1L)),
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ftol_rel = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1L)),
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ftol_abs = p_dbl(default = 0, lower = 0, upper = Inf, special_vals = list(-1L)),
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minf_max = p_dbl(default = -Inf),
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restart_strategy = p_fct(levels = c("none", "random"), init = "random"),
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max_restarts = p_int(lower = 0L),
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catch_errors = p_lgl(init = TRUE)
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)
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private$.param_set = param_set
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},
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#' @description
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#' Optimize the acquisition function.
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#'
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#' @return [data.table::data.table()] with 1 row per candidate.
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optimize = function() {
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pv = self$param_set$values
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restart_strategy = pv$restart_strategy
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max_restarts = pv$max_restarts
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maxeval = pv$maxeval
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pv$max_restarts = NULL
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pv$restart_strategy = NULL
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pv$maxeval = NULL
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if (restart_strategy == "none") {
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max_restarts = 0L
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} else if (restart_strategy == "random" && is.null(max_restarts)) {
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max_restarts = 5 * self$acq_function$domain$length
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}
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if (is.null(maxeval)) {
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maxeval = 100 * self$acq_function$domain$length^2
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}
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wrapper = function(x, fun, constants, direction) {
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xdt = as.data.table(as.list(set_names(x, self$acq_function$domain$ids())))
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res = mlr3misc::invoke(fun, xdt = xdt, .args = constants)[[1]]
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res * direction
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}
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fun = get_private(self$acq_function)$.fun
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constants = self$acq_function$constants$values
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direction = self$acq_function$codomain$direction
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y = Inf
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n_evals = 0L
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n_restarts = 0L
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while (n_evals < maxeval || maxeval < 0 && n_restarts <= max_restarts) {
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n_restarts = n_restarts + 1L
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x0 = if (n_restarts == 1L) {
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as.numeric(self$acq_function$archive$best()[, self$acq_function$domain$ids(), with = FALSE])
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} else {
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# random restart
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as.numeric(generate_design_random(self$acq_function$domain, n = 1)$data)
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}
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optimize = function() {
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invoke(nloptr::nloptr,
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eval_f = wrapper,
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lb = self$acq_function$domain$lower,
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ub = self$acq_function$domain$upper,
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opts = insert_named(pv, list(algorithm = "NLOPT_GN_DIRECT_L", maxeval = maxeval - n_evals)),
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eval_grad_f = NULL,
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x0 = x0,
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fun = fun,
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constants = constants,
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direction = direction)
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}
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if (pv$catch_errors) {
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tryCatch({
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res = optimize()
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}, error = function(error_condition) {
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lg$warn(error_condition$message)
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stop(set_class(list(message = error_condition$message, call = NULL), classes = c("acq_optimizer_error", "mbo_error", "error", "condition")))
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})
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} else {
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res = optimize()
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}
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if (res$objective < y) {
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y = res$objective
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x = res$solution
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}
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n_evals = n_evals + res$iterations
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self$state = c(self$state, set_names(list(list(model = res, start = x0)), paste0("iteration_", n_restarts)))
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if (restart_strategy == "none") break
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}
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as.data.table(as.list(set_names(c(x, y * direction), c(self$acq_function$domain$ids(), self$acq_function$codomain$ids()))))
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}
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),
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active = list(
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#' @template field_print_id
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print_id = function(rhs) {
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assert_ro_binding(rhs)
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"(OptimizerDirect)"
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}
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)
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)
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mlr_acqoptimizers$add("direct", AcqOptimizerDirect)

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