diff --git a/DESCRIPTION b/DESCRIPTION index a16e07eab..ce749fcef 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,89 +1,49 @@ Package: mlrMBO Title: Bayesian Optimization and Model-Based Optimization of Expensive - Black-Box Functions -Version: 1.1.5-9000 -Authors@R: - c(person(given = "Bernd", - family = "Bischl", - role = "aut", - email = "bernd_bischl@gmx.net", - comment = c(ORCID = "0000-0001-6002-6980")), - person(given = "Jakob", - family = "Richter", - role = c("aut", "cre"), - email = "code@jakob-r.de", - comment = c(ORCID = "0000-0003-4481-5554")), - person(given = "Jakob", - family = "Bossek", - role = "aut", - email = "j.bossek@gmail.com", - comment = c(ORCID = "0000-0002-4121-4668")), - person(given = "Daniel", - family = "Horn", - role = "aut", - email = "daniel.horn@tu-dortmund.de"), - person(given = "Michel", - family = "Lang", - role = "aut", - email = "michellang@gmail.com", - comment = c(ORCID = "0000-0001-9754-0393")), - person(given = "Janek", - family = "Thomas", - role = "aut", - email = "janek.thomas@stat.uni-muenchen.de", - comment = c(ORCID = "0000-0003-4511-6245"))) -Description: Flexible and comprehensive R toolbox for model-based - optimization ('MBO'), also known as Bayesian optimization. It - implements the Efficient Global Optimization Algorithm and is designed - for both single- and multi- objective optimization with mixed - continuous, categorical and conditional parameters. The machine - learning toolbox 'mlr' provide dozens of regression learners to model - the performance of the target algorithm with respect to the parameter - settings. It provides many different infill criteria to guide the - search process. Additional features include multi-point batch - proposal, parallel execution as well as visualization and - sophisticated logging mechanisms, which is especially useful for - teaching and understanding of algorithm behavior. 'mlrMBO' is - implemented in a modular fashion, such that single components can be - easily replaced or adapted by the user for specific use cases. + Black-Box Functions +Version: 1.1.5.1 +Description: Flexible and comprehensive R toolbox for model-based optimization + ('MBO'), also known as Bayesian optimization. It implements the Efficient + Global Optimization Algorithm and is designed for both single- and multi- + objective optimization with mixed continuous, categorical and conditional + parameters. The machine learning toolbox 'mlr' provide dozens of regression + learners to model the performance of the target algorithm with respect to + the parameter settings. It provides many different infill criteria to guide + the search process. Additional features include multi-point batch proposal, + parallel execution as well as visualization and sophisticated logging + mechanisms, which is especially useful for teaching and understanding of + algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that + single components can be easily replaced or adapted by the user for specific + use cases. +Authors@R: c( + person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role = c("aut"), comment = c(ORCID = "0000-0001-6002-6980")), + person("Jakob", "Richter", email = "code@jakob-r.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4481-5554")), + person("Jakob", "Bossek", email = "j.bossek@gmail.com", role = "aut", comment = c(ORCID = "0000-0002-4121-4668")), + person("Daniel", "Horn", email = "daniel.horn@tu-dortmund.de", role = "aut"), + person("Michel", "Lang", email = "michellang@gmail.com", role = "aut", comment = c(ORCID = "0000-0001-9754-0393")), + person("Janek", "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "aut", comment = c(ORCID = "0000-0003-4511-6245"))) License: BSD_2_clause + file LICENSE URL: https://github.com/mlr-org/mlrMBO BugReports: https://github.com/mlr-org/mlrMBO/issues -Depends: - mlr (>= 2.10), - ParamHelpers (>= 1.10), - smoof (>= 1.5.1) -Imports: - backports (>= 1.1.0), - BBmisc (>= 1.11), - checkmate (>= 1.8.2), - data.table, - lhs, - parallelMap (>= 1.3) -Suggests: - akima, - cmaesr (>= 1.0.3), - covr, - DiceKriging, - earth, - emoa, - GGally, - ggplot2, - gridExtra, - kernlab, - kknn, - knitr, - mco, - nnet, - party, - randomForest, - reshape2, - rgenoud, - rmarkdown, - rpart, - testthat -VignetteBuilder: - knitr -ByteCompile: yes +Depends: mlr (>= 2.10), ParamHelpers (>= 1.10), smoof (>= 1.5.1) +Imports: backports (>= 1.1.0), BBmisc (>= 1.11), checkmate (>= 1.8.2), + data.table, lhs, parallelMap (>= 1.3) +Suggests: cmaesr (>= 1.0.3), ggplot2, DiceKriging, earth, emoa, GGally, + gridExtra, kernlab, kknn, knitr, mco, nnet, party, + randomForest, reshape2, rmarkdown, rgenoud, rpart, testthat, + covr Encoding: UTF-8 +ByteCompile: yes RoxygenNote: 7.1.1 +VignetteBuilder: knitr +NeedsCompilation: yes +Packaged: 2022-07-04 07:35:16 UTC; ripley +Author: Bernd Bischl [aut] (), + Jakob Richter [aut, cre] (), + Jakob Bossek [aut] (), + Daniel Horn [aut], + Michel Lang [aut] (), + Janek Thomas [aut] () +Maintainer: Jakob Richter +Repository: CRAN +Date/Publication: 2022-07-04 08:50:50 UTC diff --git a/R/doc_mbo_OptPath.R b/R/doc_mbo_OptPath.R index 5a07326e8..4ec5fab7e 100644 --- a/R/doc_mbo_OptPath.R +++ b/R/doc_mbo_OptPath.R @@ -11,10 +11,10 @@ #' \item{prop.type}{Type of point proposal. Possible values are #' \describe{ #' \item{initdesign}{Points actually not proposed, but in the initial design.} -#' \item{infill\_x}{Here x is a placeholder for the selected infill criterion, e.g., infill\_ei for expected improvement.} -#' \item{random\_interleave}{Uniformly sampled points added additionally to the proposed points.} -#' \item{random\_filtered}{If filtering of proposed points located too close to each other is active, these are replaced by random points.} -#' \item{final\_eval}{If \code{final.evals} is set in \code{\link{makeMBOControl}}: Final evaluations of the proposed solution to reduce noise in y.} +#' \item{infill_x}{Here x is a placeholder for the selected infill criterion, e.g., infill_ei for expected improvement.} +#' \item{random_interleave}{Uniformly sampled points added additionally to the proposed points.} +#' \item{random_filtered}{If filtering of proposed points located too close to each other is active, these are replaced by random points.} +#' \item{final_eval}{If \code{final.evals} is set in \code{\link{makeMBOControl}}: Final evaluations of the proposed solution to reduce noise in y.} #' } #' } #' \item{parego.weight}{Weight vector sampled for multi-point ParEGO} diff --git a/man/mbo_OptPath.Rd b/man/mbo_OptPath.Rd index 7fd10f869..ea0c8ae4c 100644 --- a/man/mbo_OptPath.Rd +++ b/man/mbo_OptPath.Rd @@ -14,10 +14,10 @@ The extras are: \item{prop.type}{Type of point proposal. Possible values are \describe{ \item{initdesign}{Points actually not proposed, but in the initial design.} - \item{infill\_x}{Here x is a placeholder for the selected infill criterion, e.g., infill\_ei for expected improvement.} - \item{random\_interleave}{Uniformly sampled points added additionally to the proposed points.} - \item{random\_filtered}{If filtering of proposed points located too close to each other is active, these are replaced by random points.} - \item{final\_eval}{If \code{final.evals} is set in \code{\link{makeMBOControl}}: Final evaluations of the proposed solution to reduce noise in y.} + \item{infill_x}{Here x is a placeholder for the selected infill criterion, e.g., infill_ei for expected improvement.} + \item{random_interleave}{Uniformly sampled points added additionally to the proposed points.} + \item{random_filtered}{If filtering of proposed points located too close to each other is active, these are replaced by random points.} + \item{final_eval}{If \code{final.evals} is set in \code{\link{makeMBOControl}}: Final evaluations of the proposed solution to reduce noise in y.} } } \item{parego.weight}{Weight vector sampled for multi-point ParEGO}