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https://github.com/mlr-org/bbotk
Black-box optimization framework for R.
https://github.com/mlr-org/bbotk
bbotk black-box-optimization data-science hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 optimization r r-package
Last synced: 9 days ago
JSON representation
Black-box optimization framework for R.
- Host: GitHub
- URL: https://github.com/mlr-org/bbotk
- Owner: mlr-org
- License: lgpl-3.0
- Created: 2019-12-13T15:53:27.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-05-01T06:50:04.000Z (6 months ago)
- Last Synced: 2024-05-01T09:37:21.108Z (6 months ago)
- Topics: bbotk, black-box-optimization, data-science, hyperparameter-optimization, hyperparameter-tuning, machine-learning, mlr3, optimization, r, r-package
- Language: R
- Homepage: https://bbotk.mlr-org.com
- Size: 20.2 MB
- Stars: 19
- Watchers: 8
- Forks: 9
- Open Issues: 10
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
library(bbotk)
lgr::get_logger("bbotk")$set_threshold("warn")
set.seed(1)
options(
datatable.print.nrows = 10,
datatable.print.class = FALSE,
datatable.print.keys = FALSE,
width = 100)
```
# bbotk - Black-Box Optimization Toolkit
Package website: [release](https://bbotk.mlr-org.com/) | [dev](https://bbotk.mlr-org.com/dev/)
[](https://github.com/mlr-org/bbotk/actions/workflows/r-cmd-check.yml)
[](https://cran.r-project.org/package=bbotk)
[](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
*bbotk* is a black-box optimization framework for R.
It features highly configurable search spaces via the [paradox](https://github.com/mlr-org/paradox) package and optimizes every user-defined objective function.
The package includes several optimization algorithms e.g. Random Search, Grid Search, Iterated Racing, Bayesian Optimization (in [mlr3mbo](https://github.com/mlr-org/mlr3mbo)) and Hyperband (in [mlr3hyperband](https://github.com/mlr-org/mlr3hyperband)).
bbotk is the base package of [mlr3tuning](https://github.com/mlr-org/mlr3tuning), [mlr3fselect](https://github.com/mlr-org/mlr3fselect) and [miesmuschel](https://github.com/mlr-org/miesmuschel).
## Resources
There are several sections about black-box optimization in the [mlr3book](https://mlr3book.mlr-org.com).
Often the sections about tuning are also relevant for general black-box optimization.
* Getting started with [black-box optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-black-box-optimization).
* An overview of all optimizers and tuners can be found on our [website](https://mlr-org.com/tuners.html).
* Learn about log transformations in the [search space](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-logarithmic-transformations).
* Or more advanced [search space transformations](https://mlr3book.mlr-org.com/chapters/chapter4/hyperparameter_optimization.html#sec-tune-trafo).
* Run [multi-objective optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-multi-metrics-tuning).
* The [mlr3viz](https://github.com/mlr-org/mlr3viz) package can be used to [visualize](https://mlr-org.com/gallery/technical/2022-12-22-mlr3viz/#tuning-instance) the optimization process.
* Quick optimization with the [`bb_optimize`](https://bbotk.mlr-org.com/reference/bb_optimize.html) function.
## Installation
Install the latest release from CRAN.
```{r eval = FALSE}
install.packages("bbotk")
```
Install the development version from GitHub.
```{r eval = FALSE}
pak::pkg_install("mlr-org/bbotk")
```
## Example
```{r}
# define the objective function
fun = function(xs) {
- (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10
}
# set domain
domain = ps(
x1 = p_dbl(-10, 10),
x2 = p_dbl(-5, 5)
)
# set codomain
codomain = ps(
y = p_dbl(tags = "maximize")
)
# create objective
objective = ObjectiveRFun$new(
fun = fun,
domain = domain,
codomain = codomain,
properties = "deterministic"
)
# initialize instance
instance = oi(
objective = objective,
terminator = trm("evals", n_evals = 20)
)
# load optimizer
optimizer = opt("gensa")
# trigger optimization
optimizer$optimize(instance)
# best performing configuration
instance$result
# all evaluated configuration
as.data.table(instance$archive)
```