https://github.com/d4l3k/go-bayesopt
A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.
https://github.com/d4l3k/go-bayesopt
bayesianoptimization bayesopt blackbox-optimizer gaussian-processes go hyperparameter-optimization machine-learning optimization
Last synced: about 1 month ago
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A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.
- Host: GitHub
- URL: https://github.com/d4l3k/go-bayesopt
- Owner: d4l3k
- License: mit
- Created: 2017-09-18T22:09:50.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-05-31T10:23:56.000Z (12 months ago)
- Last Synced: 2025-03-26T00:03:25.744Z (about 2 months ago)
- Topics: bayesianoptimization, bayesopt, blackbox-optimizer, gaussian-processes, go, hyperparameter-optimization, machine-learning, optimization
- Language: Go
- Homepage: https://godoc.org/github.com/d4l3k/go-bayesopt
- Size: 36.1 KB
- Stars: 50
- Watchers: 4
- Forks: 8
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# go-bayesopt [](https://travis-ci.org/d4l3k/go-bayesopt) [](https://godoc.org/github.com/d4l3k/go-bayesopt)
A library for doing Bayesian Optimization using Gaussian Processes (blackbox
optimizer) in Go/Golang.
This project is under active development, if you find a bug, or anything that
needs correction, please let me know.
## Simple Example
```go
package main
import (
"log"
"math"
"github.com/d4l3k/go-bayesopt"
)
func main() {
X := bayesopt.UniformParam{
Max: 10,
Min: -10,
}
o := bayesopt.New(
[]Param{
X,
},
)
// minimize x^2+1
x, y, err := o.Optimize(func(params map[Param]float64) float64 {
return math.Pow(params[X], 2) + 1
})
if err != nil {
log.Fatal(err)
}
log.Println(x, y)
}
```
## How does it work?
From https://github.com/fmfn/BayesianOptimization:
Bayesian optimization works by constructing a posterior distribution of
functions (gaussian process) that best describes the function you want to
optimize. As the number of observations grows, the posterior distribution
improves, and the algorithm becomes more certain of which regions in parameter
space are worth exploring and which are not, as seen in the picture below.
As you iterate over and over, the algorithm balances its needs of exploration
and exploitation taking into account what it knows about the target function. At
each step a Gaussian Process is fitted to the known samples (points previously
explored), and the posterior distribution, combined with a exploration strategy
(such as UCB (Upper Confidence Bound), or EI (Expected Improvement)), are used
to determine the next point that should be explored (see the gif below).
This process is designed to minimize the number of steps required to find a
combination of parameters that are close to the optimal combination. To do so,
this method uses a proxy optimization problem (finding the maximum of the
acquisition function) that, albeit still a hard problem, is cheaper (in the
computational sense) and common tools can be employed. Therefore Bayesian
Optimization is most adequate for situations where sampling the function to be
optimized is a very expensive endeavor. See the references for a proper
discussion of this method.
## License
go-bayesopt is licensed under the MIT license.