Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jmejia8/hypertuning.jl
HyperTuning: Automated hyperparameter tuning in Julia.
https://github.com/jmejia8/hypertuning.jl
hyperparameter-optimization julia-language multiinstance
Last synced: 5 days ago
JSON representation
HyperTuning: Automated hyperparameter tuning in Julia.
- Host: GitHub
- URL: https://github.com/jmejia8/hypertuning.jl
- Owner: jmejia8
- License: mit
- Created: 2022-12-28T02:12:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-23T21:40:06.000Z (over 1 year ago)
- Last Synced: 2024-11-09T08:03:10.222Z (5 days ago)
- Topics: hyperparameter-optimization, julia-language, multiinstance
- Language: Julia
- Homepage: https://jmejia8.github.io/HyperTuning.jl
- Size: 742 KB
- Stars: 19
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# HyperTuning.jl
[Installation](#installation) /
[Quick Start](#quick-start) /
[Features](#features) /
[Examples](#examples) /
[Documentation](https://jmejia8.github.io/HyperTuning.jl/dev/)
Automated hyperparameter tuning in Julia.
HyperTuning aims to be intuitive, capable of handling multiple problem instances, and providing easy parallelization.
[](https://github.com/JuliaTesting/Aqua.jl)
[](https://jmejia8.github.io/HyperTuning.jl/dev/)
## Installation
This package can be installed on Julia v1.7 and above. Use one of the following options.
Via Pkg module:
```julia
julia> import Pkg; Pkg.add("HyperTuning")
```
Via the Julia package manager, type `]` and
```
pkg> add HyperTuning
```
## Quick Start
Let's begin `using HyperTuning` to optimize $f(x,y)=(1-x)^2+(y-1)^2$.
After that, the hyperparameters and budget are given in a new scenario.
Once the scenario and the objective function are defined, the optimization process begins.
```julia
julia> using HyperTuning
julia> function objective(trial)
@unpack x, y = trial
(1 - x)^2 + (y - 1)^2
end
objective (generic function with 1 method)
julia> scenario = Scenario(x = (-10.0..10.0),
y = (-10.0..10.0),
max_trials = 200);
julia> HyperTuning.optimize(objective, scenario)
Scenario: evaluated 200 trials.
parameters: x, y
space cardinality: Huge!
instances: 1
batch_size: 8
sampler: BCAPSampler{Random.Xoshiro}
pruner: NeverPrune
max_trials: 200
max_evals: 200
stop_reason: HyperTuning.BudgetExceeded("Due to max_trials")
best_trial:
┌───────────┬────────────┐
│ Trial │ Value │
│ 198 │ │
├───────────┼────────────┤
│ x │ 0.996266 │
│ y │ 1.00086 │
│ Pruned │ false │
│ Success │ false │
│ Objective │ 1.46779e-5 │
└───────────┴────────────┘
julia> @unpack x, y = scenario
```
See [here](https://github.com/jmejia8/hypertuning-examples) for more examples.
## Features
- **Intuitive usage**: Define easily the objective function, the hyperparameters, start optimization, and nothing more.
- **Muti-instance**: Find the best hyperparameters, not for a single application but multiple problem instances, datasets, etc.
- **Parallelization**: Don't worry, simply start `julia -t8` if you have 8 available threads or `julia -p4` if you want 4 distributed processes, and the HyperTuning does the rest.
- **Parameters**: This package is compatible with integer, float, boolean, and categorical parameters; however permutations and vectors of numerical values are compatible.
- **Samplers**: `BCAPSampler` for a heuristic search, `GridSampler` for brute force, and `RandomSampler` for an unbiased search.
- **Pruner**: `NeverPrune` to never prune a trial and `MedianPruner` for early-stopping the algorithm being configured.
## Examples
Examples for different Julia packages.
- Optimization
* [Metaheuristics](https://github.com/jmejia8/hypertuning-examples/blob/main/Metaheuristics/metaheuristics.jl): The best parameters for a metaheuristic.
* [Optim](https://github.com/jmejia8/hypertuning-examples/blob/main/Optim/optim.jl): The best parameter for an exact optimizer.
- Machine Learning
* [MLJ](https://github.com/jmejia8/hypertuning-examples/blob/main/MLJ/mlj.jl): The best hyperparameters for a Machine Learning method.
* [Flux](https://github.com/jmejia8/hypertuning-examples/blob/main/Flux/flux.jl): The best hyperparameters for an artificial neural network method.
Further examples can be found at [https://github.com/jmejia8/hypertuning-examples](https://github.com/jmejia8/hypertuning-examples)
## Citation
Please, cite us if you use this package in your research work.
> Mejía-de-Dios, JA., Mezura-Montes, E. & Quiroz-Castellanos, M. Automated parameter tuning as a bilevel optimization problem solved by a surrogate-assisted population-based approach. Appl Intell 51, 5978–6000 (2021). https://doi.org/10.1007/s10489-020-02151-y
```bibtex
@article{MejadeDios2021,
author = {Jes{\'{u}}s-Adolfo Mej{\'{\i}}a-de-Dios and Efr{\'{e}}n Mezura-Montes and Marcela Quiroz-Castellanos},
title = {Automated parameter tuning as a bilevel optimization problem solved by a surrogate-assisted population-based approach},
journal = {Applied Intelligence}
doi = {10.1007/s10489-020-02151-y},
url = {https://doi.org/10.1007/s10489-020-02151-y},
year = {2021},
month = jan,
publisher = {Springer Science and Business Media {LLC}},
volume = {51},
number = {8},
pages = {5978--6000},
}
```
## Contributing
To start contributing to the codebase, consider opening an issue describing the possible changes.
PRs fixing typos or grammar issues are always welcome.