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| author | Mădălin Ionel Patrașcu <madalinionel.patrascu@mdc-berlin.de> | 2024-05-08 03:02:49 +0200 |
|---|---|---|
| committer | Ricardo Wurmus <rekado@elephly.net> | 2024-05-08 15:07:04 +0200 |
| commit | 77fa0ca7c01e520c35ca75174e26c568318c7074 (patch) | |
| tree | 54ffc160696fe92252e77c9d2b725d8cd0c61bf7 | |
| parent | 113a6271e59b7a0ffc97c697025a9f6fd7eb5ab6 (diff) | |
| download | guix-patches-77fa0ca7c01e520c35ca75174e26c568318c7074.tar guix-patches-77fa0ca7c01e520c35ca75174e26c568318c7074.tar.gz | |
gnu: Add r-mlr3mbo.
* gnu/packages/cran.scm (r-mlr3mbo): New variable.
Change-Id: I23d7af239f7ab55599faf99d5136621cc45c973f
| -rw-r--r-- | gnu/packages/cran.scm | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 03373d4cd2..356409ab4c 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -38047,6 +38047,42 @@ includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.") (license license:lgpl3))) +(define-public r-mlr3mbo + (package + (name "r-mlr3mbo") + (version "0.2.2") + (source + (origin + (method url-fetch) + (uri (cran-uri "mlr3mbo" version)) + (sha256 + (base32 "0jdj5dx6jb7n0g950h0j8jhafdj5mcalv5vxfiyf07myr6mjipri")))) + (properties `((upstream-name . "mlr3mbo"))) + (build-system r-build-system) + (propagated-inputs (list r-bbotk + r-checkmate + r-data-table + r-lgr + r-mlr3 + r-mlr3misc + r-mlr3tuning + r-paradox + r-r6 + r-spacefillr)) + (native-inputs (list r-knitr)) + (home-page "https://mlr3mbo.mlr-org.com") + (synopsis "Flexible Bayesian optimization") + (description + "This package provides a flexible approach to Bayesian optimization / model +based optimization building on the bbotk package. The mlr3mbo is a toolbox +providing both ready-to-use optimization algorithms as well as their fundamental +building blocks allowing for straightforward implementation of custom algorithms. +Single- and multi-objective optimization is supported as well as mixed continuous, +categorical and conditional search spaces. Moreover, using mlr3mbo for +hyperparameter optimization of machine learning models within the mlr3 ecosystem +is straightforward via mlr3tuning.") + (license license:lgpl3))) + (define-public r-mlr3measures (package (name "r-mlr3measures") |