mlrMBO

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Science Score: 33.0%

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CITATION.cff file
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DOI references
Found 7 DOI reference(s) in README
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Links to: arxiv.org, springer.com
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8 of 23 committers (34.8%) from academic institutions
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Low similarity (14.9%) to scientific vocabulary

Keywords

bayesian-optimization black-box-optimization hyperparameter-optimization mlr mlrmbo model-based-optimization optimization r r-package

Keywords from Contributors

learners imbalance-correction hyperparameters-optimization feature-selection multilabel-classification predictive-modeling stacking survival-analysis reproducibility parallel-computing

Last synced: 6 months ago · JSON representation

Repository

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Basic Info

Host: GitHub
Owner: mlr-org
License: other
Language: R
Default Branch: main
Homepage: https://mlrmbo.mlr-org.com
Size: 77.5 MB

Statistics

Stars: 188
Watchers: 30
Forks: 47
Open Issues: 95
Releases: 1

Topics

bayesian-optimization black-box-optimization hyperparameter-optimization mlr mlrmbo model-based-optimization optimization r r-package

Created over 12 years ago · Last pushed over 2 years ago

Metadata Files

Readme License

README.Rmd

---
output: github_document
---

```{r setup, include=FALSE}
library(knitr)
library(gifski)
opts_knit$set(upload.fun = imgur_upload, base.url = NULL) # upload all images to imgur.com
opts_chunk$set(fig.width=5, fig.height=5, cache=TRUE)
```

# mlrMBO

Package website: [mlrmbo.mlr-org.com](https://mlrmbo.mlr-org.com/)

Model-based optimization with [mlr](https://github.com/mlr-org/mlr/).


[![tic](https://github.com/mlr-org/mlrMBO/workflows/tic/badge.svg?branch=master)](https://github.com/mlr-org/mlrMBO/actions)
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/mlrMBO)](https://cran.r-project.org/package=mlrMBO)
[![Coverage Status](https://img.shields.io/codecov/c/github/mlr-org/mlrMBO/master.svg)](https://codecov.io/github/mlr-org/mlrMBO?branch=master)
[![Monthly RStudio CRAN Downloads](https://cranlogs.r-pkg.org/badges/mlrMBO)](https://CRAN.R-project.org/package=mlrMBO)


* [Documentation](https://mlrmbo.mlr-org.com/)
* [Issues, Requests and Bug Tracker](https://github.com/mlr-org/mlrMBO/issues)

# Installation

We recommend to install the official release version:

```{r, eval = FALSE}
install.packages("mlrMBO")
```

For experimental use you can install the latest development version:

```{r, eval = FALSE}
remotes::install_github("mlr-org/mlrMBO")
```

# Introduction

```{r animation, message = FALSE, warning = FALSE, echo=FALSE, eval=TRUE, fig.width=7, fig.height=4, animation.hook='gifski'}
set.seed(2)
library(ggplot2)
library(mlrMBO)
library(animation)
configureMlr(show.learner.output = FALSE)
pause = interactive()
set.seed(1)

fn = makeCosineMixtureFunction(1)
obj.fun = convertToMinimization(fn)
# mbo control with defaults

ctrl = makeMBOControl()
ctrl = setMBOControlTermination(ctrl, iters = 10L)
ctrl = setMBOControlInfill(ctrl, crit = makeMBOInfillCritEI(), opt = "focussearch", opt.focussearch.points = 500L, opt.restarts = 1L)

design = generateDesign(5L, getParamSet(obj.fun), fun = lhs::maximinLHS)

run = exampleRun(obj.fun, design = design,
  control = ctrl, points.per.dim = 1000, show.info = TRUE)

for(i in 1:10) {
  plotExampleRun(run, iters = i, pause = pause, densregion = TRUE, gg.objects = list(theme_bw()))
}
```

`mlrMBO` is a highly configurable R toolbox for model-based / Bayesian optimization of black-box functions.

Features:

* EGO-type algorithms (Kriging with expected improvement) on purely numerical search spaces, see [Jones et al. (1998)](https://link.springer.com/article/10.1023/A:1008306431147)
* Mixed search spaces with numerical, integer, categorical and subordinate parameters
* Arbitrary parameter transformation allowing to optimize on, e.g., logscale
* Optimization of noisy objective functions
* Multi-Criteria optimization with approximated Pareto fronts
* Parallelization through multi-point batch proposals
* Parallelization on many parallel back-ends and clusters through [batchtools](https://github.com/mllg/batchtools) and [parallelMap](https://github.com/mlr-org/parallelMap)

For the *surrogate*, `mlrMBO` allows any regression learner from [`mlr`](https://github.com/mlr-org/mlr), including:

* Kriging aka. Gaussian processes (i.e. `DiceKriging`)
* random Forests (i.e. `randomForest`)
* and many more...

Various *infill criteria* (aka. _acquisition functions_) are available:

* Expected improvement (EI)
* Upper/Lower confidence bound (LCB, aka. statistical lower or upper bound)
* Augmented expected improvement (AEI)
* Expected quantile improvement (EQI)
* API for custom infill criteria

Objective functions are created with package [smoof](https://github.com/jakobbossek/smoof), which also offers many test functions for example runs or benchmarks.

Parameter spaces and initial designs are created with package [ParamHelpers](https://github.com/mlr-org/ParamHelpers).


# How to Cite

Please cite our [arxiv paper](https://arxiv.org/abs/1703.03373) (Preprint).
You can get citation info via `citation("mlrMBO")` or copy the following BibTex entry:

```bibtex
@article{mlrMBO,
  title = {{{mlrMBO}}: {{A Modular Framework}} for {{Model}}-{{Based Optimization}} of {{Expensive Black}}-{{Box Functions}}},
  url = {https://arxiv.org/abs/1703.03373},
  shorttitle = {{{mlrMBO}}},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1703.03373},
  primaryClass = {stat},
  author = {Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel},
  date = {2017-03-09},
}
```

Some parts of the package were created as part of other publications.
If you use these parts, please cite the relevant work appropriately:

* Multi-point proposals, including the new multi-objective infill criteria: [MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization](https://doi.org/10.1007/978-3-319-09584-4_17)
* Multi-objective optimization: [Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark](https://doi.org/10.1007/978-3-319-15934-8_5)
* Multi-objective optimization with categorical variables using the random forest as a surrogate: [Multi-objective parameter configuration of machine learning algorithms using model-based optimization](https://doi.org/10.1109/SSCI.2016.7850221)

Owner

Name: mlr-org
Login: mlr-org
Kind: organization
Location: Munich, Germany

Website: https://mlr-org.com
Repositories: 80
Profile: https://github.com/mlr-org

GitHub Events

Total

Watch event: 1

Last Year

Watch event: 1

Committers

Last synced: 9 months ago

All Time

Total Commits: 1,476
Total Committers: 23
Avg Commits per committer: 64.174
Development Distribution Score (DDS): 0.698

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Jakob Richter	c**e@j**e	446
Bernd Bischl	b**l@g**t	342
Jakob Bossek	i**o@j**e	243
Daniel Horn	d**n@t**e	142
Michel	m**g@g**m	115
pat-s	p**z@g**m	50
Travis CI		38
Karin Schork	s**n@g**m	30
ja-thomas	j****s	23
nbauer2	b**r@b**e	11
Bernd Bischl	y**u@e**m	8
Jakob Bossek	b**k@w**e	7
github-actions[bot]	4****]	4
Tobias	w**r@i**e	4
mb706	m****6	3
unknown	w**r@P**e	3
Benjamin Klepper	b**r@g**m	1
Dirk	s**n@s**e	1
Katrin Leinweber	9****r	1
Pascal Kerschke	p**e@u**e	1
Jakob Richter	r**r@s**e	1
Daniel Horn	d**n@l**e	1
verenamayer	v**y@g**m	1

Committer Domains (Top 20 + Academic)

lidong1.itmc.tu-dortmund.de: 1 shell.statistik.uni-dortmund.de: 1 uni-dortmund.de: 1 statistik.tu-dortmund.de: 1 pc-342.isf.maschinenbau.uni-dortmund.de: 1 isf.de: 1 wi.uni-muenster.de: 1 bauer2.fb05.statistik.uni-dortmund.de: 1 tu-dortmund.de: 1 jakobbossek.de: 1 gmx.net: 1 jakob-r.de: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time

Total issues: 62
Total pull requests: 39
Average time to close issues: 3 months
Average time to close pull requests: 28 days
Total issue authors: 30
Total pull request authors: 6
Average comments per issue: 2.89
Average comments per pull request: 0.74
Merged pull requests: 24
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

mb706 (13)
swaheera (5)
zkurtz (5)
jakob-r (3)
rodemann (3)
Seager1989 (3)
pat-s (2)
RPdavies (2)
vrodriguezf (2)
zjujdj (2)
dipenpatel235 (2)
robsmith11 (2)
dagola (1)
pfistfl (1)
aminevsaziz (1)

Pull Request Authors

jakob-r (24)
pat-s (8)
ja-thomas (3)
mb706 (2)
berndbischl (1)
elseverzeynal (1)

Top Labels

Issue Labels

documentation (4) bug (2) prio-medium (2) prio-high (2) enhancement (1)

Pull Request Labels

enhancement (4) work in progress (3) ready for merge (?) (2) prio-low (1) prio-high (1) later (1) prio-medium (1)

Packages

Total packages: 4
Total downloads:
- cran 2,524 last-month
Total docker downloads: 47,146

Total dependent packages: 9
(may contain duplicates)
Total dependent repositories: 15
(may contain duplicates)
Total versions: 15
Total maintainers: 1

proxy.golang.org: github.com/mlr-org/mlrmbo

Documentation: https://pkg.go.dev/github.com/mlr-org/mlrmbo#section-documentation
License: other
Latest release: v1.1.1
published about 8 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.5%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

proxy.golang.org: github.com/mlr-org/mlrMBO

Documentation: https://pkg.go.dev/github.com/mlr-org/mlrMBO#section-documentation
License: other
Latest release: v1.1.1
published about 8 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.5%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

cran.r-project.org: mlrMBO

Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions

Homepage: https://github.com/mlr-org/mlrMBO
Documentation: http://cran.r-project.org/web/packages/mlrMBO/mlrMBO.pdf
License: BSD_2_clause + file LICENSE
Latest release: 1.1.5
published over 5 years ago

Versions: 8
Dependent Packages: 8
Dependent Repositories: 15
Downloads: 2,524 Last month
Docker Downloads: 47,146

Rankings

Forks count: 1.4%

Stargazers count: 2.3%

Dependent packages count: 6.1%

Dependent repos count: 7.4%

Downloads: 8.0%

Average: 8.1%

Docker downloads count: 23.2%

Maintainers (1)

code@jakob-r.de

Last synced: 6 months ago

conda-forge.org: r-mlrmbo

Homepage: https://github.com/mlr-org/mlrMBO
License: BSD-2-Clause
Latest release: 1.1.5
published over 5 years ago

Versions: 5
Dependent Packages: 1
Dependent Repositories: 0

Rankings

Forks count: 24.0%

Stargazers count: 24.9%

Average: 27.9%

Dependent packages count: 28.8%

Dependent repos count: 34.0%

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

ParamHelpers >= 1.10 depends
mlr >= 2.10 depends
smoof >= 1.5.1 depends
BBmisc >= 1.11 imports
backports >= 1.1.0 imports
checkmate >= 1.8.2 imports
data.table * imports
lhs * imports
parallelMap >= 1.3 imports
DiceKriging * suggests
GGally * suggests
akima * suggests
cmaesr >= 1.0.3 suggests
covr * suggests
earth * suggests
emoa * suggests
ggplot2 * suggests
gridExtra * suggests
interp * suggests
kernlab * suggests
kknn * suggests
knitr * suggests
mco * suggests
nnet * suggests
party * suggests
randomForest * suggests
reshape2 * suggests
rgenoud * suggests
rmarkdown * suggests
rpart * suggests
testthat * suggests