nmoo 5.1.0

A wrapper-based framework for pymoo problem modification.

noisy-moo

A wrapper-based framework for pymoo problem modification and algorithm benchmarking. Initially developed to test KNN-averaging[^quatic21].

Installation

Simply run

pip install nmoo

Getting started

In a notebook

See example.ipynb for a quick example.

For larger benchmarks

For larger benchmarks, you may want to use nmoo's CLI. First, create a module, say example.py, containing your benchmark factory (a function that returns your benchrmark), say make_benchmark(). Then, run it using

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

Refer to

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

virtualenv venv -p python3.8
. ./venv/bin/activate
pip install -r requirements.txt
pip install -r requirements.dev.txt

Documentation

Simply run

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

make docs-browser

Code quality

Don't forget to run

make

to format the code following black, typecheck it using mypy, and check it against coding standards using pylint.

[^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