moocore0.1.10
Published
Core Algorithms for Multi-Objective Optimization
pip install moocore
Package Downloads
Project URLs
Requires Python
>=3.10
Dependencies
- cffi
>=1.17.1 - numpy
>=1.23 - platformdirs
- botorch
; extra == "benchmarks" - cpuinfo
; extra == "benchmarks" - desdeo
; extra == "benchmarks" - jmetal
; extra == "benchmarks" - matplotlib
; extra == "benchmarks" - nevergrad
; extra == "benchmarks" - numba
; extra == "benchmarks" - numpy
; extra == "benchmarks" - pandas
; extra == "benchmarks" - pandas
>=2; extra == "benchmarks" - paretoset
; extra == "benchmarks" - pymoo
; extra == "benchmarks" - coverage
[toml]; extra == "coverage" - gcovr
; extra == "coverage" - pre-commit
>=3.3.2; extra == "dev" - ruff
>=0.11.2; extra == "dev" - tox
>=4.6.2; extra == "dev" - ipykernel
; extra == "docs" - ipywidgets
; extra == "docs" - jupyter
; extra == "docs" - jupyterlab
; extra == "docs" - kaleido
; extra == "docs" - pandas
>=2; extra == "docs" - plotly
; extra == "docs" - pydata-sphinx-theme
>=0.16; extra == "docs" - seaborn
; extra == "docs" - sphinx
>=6; extra == "docs" - sphinx-autodoc-typehints
; extra == "docs" - sphinx-copybutton
; extra == "docs" - sphinx-design
; extra == "docs" - sphinx-gallery
>=0.19; extra == "docs" - sphinxcontrib-bibtex
; extra == "docs" - sphinxcontrib-napoleon
; extra == "docs" - pandas
>=2; extra == "test" - pytest
>7; extra == "test"
moocore: Core Algorithms for Multi-Objective Optimization
Contributors: Manuel López-Ibáñez, Fergus Rooney.
Introduction
The goal of moocore is to collect and document fast implementations of core mathematical functions and algorithms for multi-objective optimization. These functions include:
- Generate and transform nondominated sets.
- Identify and filter dominated vectors.
- Quality metrics such as (weighted) hypervolume, epsilon, IGD, etc.
- Computation of the Empirical Attainment Function. The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space.
Keywords: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, bi-objective optimization, performance measures, performance assessment
For more details, see the complete Documentation.
Install
You can install the latest release using pip, for example:
python3 -m pip install moocore
Or to build the latest development version from GitHub:
python3 -m pip install 'git+https://github.com/multi-objective/moocore.git#egg=moocore&subdirectory=python'
Building the development version requires a C/C++ compiler. Instead, you can install pre-compiled development wheels for your operating system. See the list of wheels here (https://github.com/multi-objective/moocore/tree/wheels), click in the wheel you wish to install then copy the View Raw link. For example,
python3 -m pip install https://github.com/multi-objective/moocore/raw/refs/heads/wheels/moocore-0.1.5.dev0-py3-none-macosx_10_9_universal2.whl
If the URL does not have the word raw then you are not using the View Raw link.
R package
There is also a moocore package for R: https://multi-objective.github.io/moocore/r