https://github.com/altaris/noisy-moo

A wrapper-based framework for pymoo problem modification.
https://github.com/altaris/noisy-moo

multi-objective-optimization optimization optimization-algorithms pymoo python

Last synced: about 1 month ago
JSON representation

A wrapper-based framework for pymoo problem modification.

• Host: GitHub
• URL: https://github.com/altaris/noisy-moo
• Owner: altaris
• License: mit
• Created: 2021-05-30T06:14:39.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-03-08T02:20:34.000Z (almost 2 years ago)
• Last Synced: 2024-11-02T07:49:38.469Z (about 2 months ago)
• Topics: multi-objective-optimization, optimization, optimization-algorithms, pymoo, python
• Language: Python
• Homepage: https://altaris.github.io/noisy-moo/nmoo.html
• Size: 3.79 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

README

        
noisy-moo

=========

![Python 3](https://img.shields.io/badge/python-3-blue?logo=python)

[![MIT License](https://img.shields.io/badge/license-MIT-yellow)](https://choosealicense.com/licenses/mit/)

[![Code style](https://img.shields.io/badge/style-black-black)](https://pypi.org/project/black)

[![Maintainability](https://api.codeclimate.com/v1/badges/aedd8e97a23534a85bc5/maintainability)](https://codeclimate.com/github/altaris/noisy-moo/maintainability)

[![Documentation](https://badgen.net/badge/documentation/here/blue)](https://altaris.github.io/noisy-moo/nmoo.html)

    

A wrapper-based framework for [pymoo](https://pymoo.org/) problem modification

and algorithm benchmarking. Initially developed to test

*KNN-averaging*[^quatic21].

# Installation

Simply run

```sh

pip install nmoo

```

# Getting started

## In a notebook

See

[example.ipynb](https://github.com/altaris/noisy-moo/blob/main/example.ipynb)

for a quick example.

[![Launch Google Colab

notebook](https://img.shields.io/badge/launch-colab-blue?logo=googlecolab)](https://colab.research.google.com/github/altaris/noisy-moo/blob/main/example.ipynb)

## For larger benchmarks

For larger benchmarks, you may want to use nmoo's CLI. First, create a module,

say [`example.py`](https://github.com/altaris/noisy-moo/blob/main/example.py),

containing your benchmark factory (a function that returns your

[benchrmark](https://altaris.github.io/noisy-moo/nmoo/benchmark.html#Benchmark)),

say `make_benchmark()`. Then, run it using

```sh

python -m nmoo run --verbose 10 example:make_benchmark

```

Refer to

```sh

python -m nmoo --help

```

for more information.

# Main submodules and classes

* `nmoo.benchmark.Benchmark`: A `Benchmark` object represents... a benchmark

  🤔. At construction, you can specify problems and algorithms to run, how many

  times to run them, what performance indicators to compute, etc. Refer to

  `nmoo.benchmark.Benchmark.__init__` for more details.

* `nmoo.wrapped_problem.WrappedProblem`: The main idea of `nmoo` is to wrap

  problems in layers. Each layer should redefine `pymoo.Problem._evaluate` to

  intercept calls to the wrapped problem. It is then possible to apply/remove

  noise, keep a call history, log, etc.

* `nmoo.denoisers`: Sublasses of `nmoo.wrapped_problem.WrappedProblem` that

  implement denoising algorithms. In a simple scenario, a synthetic problem

  would be wrapped in a noise layer, and further wrapped in a denoising layer

  to test the performance of the latter.

* `nmoo.noises`: Sublasses of `nmoo.wrapped_problem.WrappedProblem` that apply

  noise.

# Contributing

## Dependencies

* `python3.8` or newer;

* `requirements.txt` for runtime dependencies;

* `requirements.dev.txt` for development dependencies (optional);

* `make` (optional).

Simply run

```sh

virtualenv venv -p python3.8

. ./venv/bin/activate

pip install -r requirements.txt

pip install -r requirements.dev.txt

```

## Documentation

Simply run

```sh

make docs

```

This will generate the HTML doc of the project, and the index file should be at

`docs/index.html`. To have it directly in your browser, run

```sh

make docs-browser

```

## Code quality

Don't forget to run

```sh

make

```

to format the code following [black](https://pypi.org/project/black/),

typecheck it using [mypy](http://mypy-lang.org/), and check it against coding

standards using [pylint](https://pylint.org/).

[^quatic21]: Klikovits, S., Arcaini, P. (2021). KNN-Averaging for Noisy

    Multi-objective Optimisation. In: Paiva, A.C.R., Cavalli, A.R., Ventura

    Martins, P., Pérez-Castillo, R. (eds) Quality of Information and

    Communications Technology. QUATIC 2021. Communications in Computer and

    Information Science, vol 1439. Springer, Cham.

    https://doi.org/10.1007/978-3-030-85347-1_36