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evobench-0.5.2
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Optimization benchmarks, both synthetic and practical.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | evobench-0.5.2 |
| نام | evobench |
| نسخه کتابخانه | 0.5.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Piotr Rarus |
| ایمیل نویسنده | piotr.rarus@gmail.com |
| آدرس صفحه اصلی | https://github.com/piotr-rarus/evobench |
| آدرس اینترنتی | https://pypi.org/project/evobench/ |
| مجوز | MIT |
[](https://codecov.io/gh/piotr-rarus/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
- TSP
- cVRP
### Discrete
- Trap
- Step Trap
- Bimodal
- Step Bimodal
- HIFF
- Ising Spin Glass
### 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,
multiprocessing=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 $B
- 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.
We're working on linkage quality metrics. Once they're published, we'll be incorporating them to this package.
نیازمندی
| مقدار | نام |
|---|---|
| >=1.4 | lazy |
| >=2.5.1 | networkx |
| >=1.16.0 | numpy |
| >=1.2.4 | pandas |
| >=4.14.3 | plotly |
| >=0.24.0 | scikit-learn |
| >=1.6.1 | scipy |
| >=4.47.0 | tqdm |
نحوه نصب
نصب پکیج whl evobench-0.5.2:
pip install evobench-0.5.2.whl
نصب پکیج tar.gz evobench-0.5.2:
pip install evobench-0.5.2.tar.gz