Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/piotr-rarus/evobench
Benchmarks for large scale, model-based optimization.
https://github.com/piotr-rarus/evobench
Last synced: 3 days ago
JSON representation
Benchmarks for large scale, model-based optimization.
- Host: GitHub
- URL: https://github.com/piotr-rarus/evobench
- Owner: piotr-rarus
- License: mit
- Created: 2020-02-13T10:11:16.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-08-23T18:40:57.000Z (about 2 years ago)
- Last Synced: 2024-10-31T06:22:07.365Z (19 days ago)
- Language: Python
- Size: 17.1 MB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://badge.fury.io/py/evobench)
[](https://codecov.io/gh/piotr-rarus/evobench)
[](https://github.com/piotr-rarus/evobench/blob/main/LICENSE)
[](https://pypi.python.org/pypi/evobench/)
# evobench
__evobench__ is a collection of benchmark problems dedicated for optimization problems (both synthetic and practical). Please note that Python isn't still best tool for solving optimization problems, as loops are still slow. This might change in a next couple of years. Our main intention is to provide easily accessible package for PoC, research or teaching purposes.
## Getting started
```sh
pip install evobench
```
```py
import evobench
trap = evobench.discrete.Trap(blocks=[4, 4, 4])
population = trap.initialize_population(population_size=1e3)
trap.evaluate_population(population)
```
Fitness evaluation produces side effect of defining solution's fitness.
```py
print(population.solutions[0].fitness)
```
You can also evaluate single solution.
```py
fitness = trap.evaluate_solution(population.solutions[0])
```
Every time you evaluate undefined solution we increment `ffe` counter.
Solution is not evaluated again, if it didn't change.
You can access it through a `benchmark` instance.
```py
print(trap.ffe)
```
## Overview
This package exposes following problems.
### Practical
### Discrete
- Bimodal
- Bimodal Noised
- HIFF
- Ising Spin Glass
- Step Trap
- Step Bimodal
- Trap
### Continuous
- Trap
- Step Trap
- Multimodal
- Step Multimodal
- Sawtooth
## Compound Benchmark
Creating your own compound benchmarks is really easy.
You just need to define your sub-benchmarks and pass them as a list. All other fuctions work just the same as with the normal `Benchmark`.
```py
from evobench import CompoundBenchmark, continuous, discrete
benchmark = CompoundBenchmark(
benchmarks=[
discrete.Trap(blocks=[5, 2, 4]),
continuous.Trap(blocks=[3, 6, 4])
],
use_shuffle=True,
verbose=1
)
population = benchmark.initialize_population(population_size=1000)
benchmark.evaluate_population(population)
```
## Ising Spin Glass
To instantiate _ISG_ you need to pass specific problem configuration.
```py
from evobench.discrete import IsingSpinGlass
isg = IsingSpinGlass('IsingSpinGlass_pm_16_0')
```
You can find 5,000 instances at `evobench\discrete\isg\data` folder. Instances vary in length and complexity.
## How to implement your own benchmark
Inherit `Benchmark` class from `evobench.benchmark`. Then implement:
- `def _evaluate_solution(self, solution: Solution) -> float`
- `def random_solutions(self, population_size: int) -> List[Solution]`
### Partially separable
You need to inherit `Separable` class from `evobench.separable`.
Then just implement:
- `def evaluate_block(self, block: np.ndarray) -> int`.
Best follow `evobench.discrete.trap` implementation.
## Linkage quality
Linkage quality metrics are located at `evobench.linkage.metrics`.
Available metrics:
- Mean Reciprocal Ranking @K
- Mean Average Precision @K
- NDCG @K
- Fill Quality
## Coming soon
We'll be adding more problems in the near future. If you're looking for any particular problem, please mail us or open an issue.