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benhidYevheniiSemendiakYebisumsteijaertbenhid
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jMetalPy v1.5.7 (#91)
* Add solution generator and evaluator for SA (#67) * Add warm startup for SA using population_generator. * Revert evaluator parametrization in SA. * Working on implementing a IntegerFloatSolution class * Update nsgaiii.py (#70) removed obsolete import * Fix conflict in file smpsorp_zdt4.py * Add new implementation of quality indicators. All of them receive a numpy array as a parameter instead of a list of solutions. * Refactor quality indicators. All of them receive as a parameter a numpy array instead of a list of solutions * Add file ZDT1.pf in the test folder (to be use to test quality indicators) * Feature/mixed solution (#73) * Working on implementing a IntegerFloatSolution class * Add unit test cases for class IntegerFloatProblem * Add class NMMin * Add class IntegerFloatSBXCrossover * Add test cases for SBXCrossover * Still working on implementing an approach for the IntegerFloatSolution class * Add user defined exceptiones in file checking.py * Working on the implementation of class CompositeSolution * Workon on class CompositeSolution * Class CompositeMutation implemented and tested * Fix a bug in class Neighborhood * Class CompositeCrossover implemented and tested * Add class * Add class * Rename file * Add problem ZDT1Modified * Add examples with NSGA-II * Add NSGA-II examples * Optimize imports * Minor changes * Changes on attribute name Co-authored-by: Yebisu <ajnebro@outlook.com> * Minor changes * Release v1.5.4 * Updating the MOEAD and variants examples * Minor changes * Add problem UF1 * Fix bug in ibea. * Refactor crossover and mutation operators * Refactor crossover and mutation operators * New minor version 1.5.5 * Adapt CXCrossover and ScrambleMutation to TSP (#80) * Added new minor version * Removed SyntaxWarning for with literals * Changed typing of frequency to int * Minor changes * Use of linters for catching errors and formatters to fix style * fix IntegerProblem cannot get upper bound value bug (#86) * optimise deepcopy for list (#87) * Added logging * Update __init__.py (#92) Export `RouletteWheelSelection` Co-authored-by: Antonio <makishinake@hotmail.com> * Minor changes * Python version 3.7 * Minor changes * Optimize imports * Minor changes * removing depricated functions (#118) * Fix #115 * fixing kursawe's function kursawe's f2 function must go through all variables * evaluate function for zdt6 class zdt6's f1 function is different from the others zdt functions * Fix style * Replace Travis with Github Actions * setup.py deprecated in favor of setup.cfg * Add develop branch for triggering options * Replace build badge Co-authored-by: Yevhenii Semendiak <32543098+YevheniiSemendiak@users.noreply.github.com> Co-authored-by: ajnebro <ajnebro@outlook.com> Co-authored-by: Marvin Steijaert <msteijaert@gmail.com> Co-authored-by: benhid <atbnhd@gmail.com> Co-authored-by: Sizhe Yuen <sizhe1007@gmail.com> Co-authored-by: Antonio J. Nebro <ajnebro@users.noreply.github.com> Co-authored-by: Yevhenii Semendiak <semendyak@gmail.com> Co-authored-by: Lingpho <bob.li.0718@gmail.com> Co-authored-by: Wojciech Lewicki <wojciech.lewicki@swmansion.com> Co-authored-by: root <root@9575ed024558> Co-authored-by: Arysson Oliveira <51703579+ASdOliveira@users.noreply.github.com> Co-authored-by: Isabela <63879870+isabelagabriely@users.noreply.github.com>
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‎.github/workflows/ci.yml‎

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name: CI
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on:
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workflow_dispatch:
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push:
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branches:
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- main
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- develop
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pull_request:
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branches:
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- main
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- develop
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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py: [ 3.9, 3.8, 3.7 ]
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steps:
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- uses: actions/checkout@v2
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- name: Install Python ${{ matrix.py }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.py }}
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- name: Get full Python version
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shell: bash
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run: echo ::set-output name=version::$(python -c "import sys; print('-'.join(str(v) for v in sys.version_info))")
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- name: Install dependencies
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shell: bash
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run: |
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python -m pip install --upgrade pip
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python -m pip install -e .[test]
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- name: Run unittest
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shell: bash
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run: python -m unittest discover -q

‎.github/workflows/release.yml‎

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name: release
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on:
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push:
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tags:
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- '*.*.*'
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jobs:
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release:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2
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- name: Install Python 3.7
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uses: actions/setup-python@v2
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with:
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python-version: 3.7
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- name: Get full Python version
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shell: bash
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run: echo ::set-output name=version::$(python -c "import sys; print('-'.join(str(v) for v in sys.version_info))")
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- name: Build project for distribution
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shell: bash
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run: |
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python -m pip install -q build
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python -m build
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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prerelease: steps.check-version.outputs.prerelease == 'true'

‎.gitignore‎

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# osx
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.DS_Store
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# R
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.RData
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.Rhistory
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# Github codespaces
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pythonenv3.8

‎.travis.yml‎

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‎Makefile‎

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# Minimal makefile for Sphinx documentation
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#
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install:
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@python setup.py build install
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# You can set these variables from the command line.
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SPHINXOPTS =
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SPHINXBUILD = sphinx-build
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SPHINXPROJ = jMetalPy
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SOURCEDIR = docs/source
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BUILDDIR = build
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install-dependencies:
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@python -m pip install -e .[all]
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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clean:
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@rm -rf build dist .eggs *.egg-info
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@find . -type d -name '.mypy_cache' -exec rm -rf {} +
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@find . -type d -name '__pycache__' -exec rm -rf {} +
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.PHONY: help Makefile
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black: clean
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@isort --profile black jmetal/ examples/
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@black jmetal/ examples/
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# "make github" option to build gh-pages
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github:
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@make html
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@cp -a $(BUILDDIR)/html/. docs
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@rm -r $(BUILDDIR)
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lint:
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@mypy jmetal/ examples/ --show-error-codes
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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tests:
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@python -m unittest discover -q

‎README.md‎

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![jMetalPy](docs/source/jmetalpy.png)
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[![Build Status](https://img.shields.io/travis/jMetal/jMetalPy/master.svg?style=flat-square)](https://travis-ci.org/jMetal/jMetalPy)
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[![Documentation](https://img.shields.io/badge/docs-online-success?style=flat-square)](https://jmetal.github.io/jMetalPy/index.html)
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[![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg?style=flat-square)]()
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[![PyPI version](https://img.shields.io/pypi/v/jMetalPy.svg?style=flat-square)]()
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[![PyPI Python version](https://img.shields.io/pypi/pyversions/jMetalPy.svg?style=flat-square)]()
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[![CI](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml/badge.svg)](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml)
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[![PyPI Python version](https://img.shields.io/pypi/pyversions/jMetalPy.svg)]()
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[![DOI](https://img.shields.io/badge/DOI-10.1016%2Fj.swevo.2019.100598-blue)](https://doi.org/10.1016/j.swevo.2019.100598)
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[![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg)]()
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
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A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598
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Other supported commands are listed next:
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```console
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pip install "jmetalpy[docs]" # Install requirements for building docs
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pip install "jmetalpy[dev]" # Install requirements for development
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pip install "jmetalpy[distributed]" # Install requirements for parallel/distributed computing
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pip install "jmetalpy[complete]" # Install all requirements
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```
@@ -100,7 +100,7 @@ plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png'
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<img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt="Pareto front approximation">
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## Features
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The current release of jMetalPy (v1.5.5) contains the following components:
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The current release of jMetalPy (v1.5.7) contains the following components:
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* Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.
106106
* Parallel computing based on Apache Spark and Dask.
@@ -118,9 +118,12 @@ The current release of jMetalPy (v1.5.5) contains the following components:
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119119
## Changelog
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121+
* [v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.
122+
* [v1.5.6] Removed warnings when using Python 3.8.
121123
* [v1.5.5] Minor bug fixes.
122124
* [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.
123125
* [v1.5.4] Added [CompositeSolution](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)
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## License
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This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.

‎docs/source/conf.py‎

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import os
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import sys
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sys.path.insert(0, os.path.abspath('../../'))
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# -- Project information -----------------------------------------------------

‎examples/experiment/comparison.py‎

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problem=problem,
2222
population_size=100,
2323
offspring_population_size=100,
24-
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
25-
distribution_index=20),
24+
mutation=PolynomialMutation(
25+
probability=1.0 / problem.number_of_variables, distribution_index=20
26+
),
2627
crossover=SBXCrossover(probability=1.0, distribution_index=20),
27-
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
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termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
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),
29-
algorithm_tag='NSGAII',
30+
algorithm_tag="NSGAII",
3031
problem_tag=problem_tag,
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run=run,
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)
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3839
population_size=100,
3940
cr=0.5,
4041
f=0.5,
41-
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
42+
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
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),
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algorithm_tag='GDE3',
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algorithm_tag="GDE3",
4445
problem_tag=problem_tag,
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run=run,
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)
@@ -50,12 +51,13 @@ def configure_experiment(problems: dict, n_run: int):
5051
algorithm=SMPSO(
5152
problem=problem,
5253
swarm_size=100,
53-
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
54-
distribution_index=20),
54+
mutation=PolynomialMutation(
55+
probability=1.0 / problem.number_of_variables, distribution_index=20
56+
),
5557
leaders=CrowdingDistanceArchive(100),
56-
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
58+
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
5759
),
58-
algorithm_tag='SMPSO',
60+
algorithm_tag="SMPSO",
5961
problem_tag=problem_tag,
6062
run=run,
6163
)
@@ -64,19 +66,19 @@ def configure_experiment(problems: dict, n_run: int):
6466
return jobs
6567

6668

67-
if __name__ == '__main__':
69+
if __name__ == "__main__":
6870
# Configure the experiments
69-
jobs = configure_experiment(problems={'ZDT1': ZDT1(), 'ZDT2': ZDT2(), 'ZDT3': ZDT3()}, n_run=25)
71+
jobs = configure_experiment(problems={"ZDT1": ZDT1(), "ZDT2": ZDT2(), "ZDT3": ZDT3()}, n_run=25)
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# Run the study
72-
output_directory = 'data'
74+
output_directory = "data"
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7476
experiment = Experiment(output_dir=output_directory, jobs=jobs)
7577
experiment.run()
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7779
# Generate summary file
7880
generate_summary_from_experiment(
7981
input_dir=output_directory,
80-
reference_fronts='resources/reference_front',
81-
quality_indicators=[GenerationalDistance(), EpsilonIndicator(), HyperVolume([1.0, 1.0])]
82+
reference_fronts="resources/reference_front",
83+
quality_indicators=[GenerationalDistance(), EpsilonIndicator(), HyperVolume([1.0, 1.0])],
8284
)

‎examples/experiment/statistical_analysis.py‎

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1-
from jmetal.lab.experiment import generate_boxplot, generate_latex_tables, compute_mean_indicator, compute_wilcoxon
1+
from jmetal.lab.experiment import (
2+
compute_mean_indicator,
3+
compute_wilcoxon,
4+
generate_boxplot,
5+
generate_latex_tables,
6+
)
27
from jmetal.lab.statistical_test.bayesian import *
38
from jmetal.lab.statistical_test.functions import *
49
from jmetal.lab.visualization import CDplot, plot_posterior
510

6-
if __name__ == '__main__':
11+
if __name__ == "__main__":
712
# Generate Median & IQR tables
8-
generate_latex_tables(filename='QualityIndicatorSummary.csv')
13+
generate_latex_tables(filename="QualityIndicatorSummary.csv")
914

1015
# Generate boxplots
11-
generate_boxplot(filename='QualityIndicatorSummary.csv')
16+
generate_boxplot(filename="QualityIndicatorSummary.csv")
1217

1318
# Wilcoxon
14-
compute_wilcoxon(filename='QualityIndicatorSummary.csv')
19+
compute_wilcoxon(filename="QualityIndicatorSummary.csv")
1520

1621
# Statistical lab
1722

18-
avg = compute_mean_indicator(filename='QualityIndicatorSummary.csv', indicator_name='HV')
23+
avg = compute_mean_indicator(filename="QualityIndicatorSummary.csv", indicator_name="HV")
1924
print(avg)
2025

2126
# Non-parametric test
22-
print('-------- Sign Test --------')
23-
print(sign_test(avg[['NSGAII', 'SMPSO']]))
24-
print('-------- Friedman Test --------')
27+
print("-------- Sign Test --------")
28+
print(sign_test(avg[["NSGAII", "SMPSO"]]))
29+
print("-------- Friedman Test --------")
2530
print(friedman_test(avg))
26-
print('-------- Friedman Aligned Rank Test --------')
31+
print("-------- Friedman Aligned Rank Test --------")
2732
print(friedman_aligned_rank_test(avg))
28-
print('-------- Quade Test --------')
33+
print("-------- Quade Test --------")
2934
print(quade_test(avg))
3035

3136
# Post-hoc tests
32-
print('-------- Friedman Post-Hoc Test --------')
33-
z, p_val, adj_pval = friedman_ph_test(avg, control=0, apv_procedure='Bonferroni')
34-
print('z values \n', z)
35-
print('p-values \n', p_val)
36-
print('adjusted p-values \n', adj_pval)
37-
print('-------- Friedman Aligned Rank Post-Hoc Test --------')
38-
z, p_val, adj_pval = friedman_aligned_ph_test(avg, apv_procedure='Shaffer')
39-
print('z values \n', z)
40-
print('p-values \n', p_val)
41-
print('adjusted p-values \n', adj_pval)
42-
print('-------- QuadeTest Post-Hoc Test --------')
43-
z, p_val, adj_pval = quade_ph_test(avg, apv_procedure='Holm')
44-
print('z values \n', z)
45-
print('p-values \n', p_val)
46-
print('adjusted p-values \n', adj_pval)
37+
print("-------- Friedman Post-Hoc Test --------")
38+
z, p_val, adj_pval = friedman_ph_test(avg, control=0, apv_procedure="Bonferroni")
39+
print("z values \n", z)
40+
print("p-values \n", p_val)
41+
print("adjusted p-values \n", adj_pval)
42+
print("-------- Friedman Aligned Rank Post-Hoc Test --------")
43+
z, p_val, adj_pval = friedman_aligned_ph_test(avg, apv_procedure="Shaffer")
44+
print("z values \n", z)
45+
print("p-values \n", p_val)
46+
print("adjusted p-values \n", adj_pval)
47+
print("-------- QuadeTest Post-Hoc Test --------")
48+
z, p_val, adj_pval = quade_ph_test(avg, apv_procedure="Holm")
49+
print("z values \n", z)
50+
print("p-values \n", p_val)
51+
print("adjusted p-values \n", adj_pval)
4752

4853
# Plot critical distance
4954

5055
CDplot(avg.T, alpha=0.15, higher_is_better=True)
5156

52-
print('-------- Bayesian Sign Test --------')
53-
bst, DProcess = bayesian_sign_test(avg[['NSGAII', 'SMPSO']], rope_limits=[-0.002, 0.002],
54-
prior_strength=0.5, return_sample=True)
55-
plot_posterior(DProcess, higher_is_better=True, alg_names=['NSGAII', 'SMPSO'])
57+
print("-------- Bayesian Sign Test --------")
58+
bst, DProcess = bayesian_sign_test(
59+
avg[["NSGAII", "SMPSO"]], rope_limits=[-0.002, 0.002], prior_strength=0.5, return_sample=True
60+
)
61+
plot_posterior(DProcess, higher_is_better=True, alg_names=["NSGAII", "SMPSO"])
5662

57-
print('Pr(NSGAII < SMPSO) = %.3f' % bst[0])
58-
print('Pr(NSGAII ~= SMPSO) = %.3f' % bst[1])
59-
print('Pr(NSGAII > SMPSO) = %.3f' % bst[2])
63+
print("Pr(NSGAII < SMPSO) = %.3f" % bst[0])
64+
print("Pr(NSGAII ~= SMPSO) = %.3f" % bst[1])
65+
print("Pr(NSGAII > SMPSO) = %.3f" % bst[2])
6066

61-
print('-------- Bayesian Signed Rank Test --------')
62-
bst, DProcess = bayesian_signed_rank_test(avg[['NSGAII', 'SMPSO']], rope_limits=[-0.002, 0.002],
63-
prior_strength=0.5, return_sample=True)
64-
plot_posterior(DProcess, higher_is_better=True, alg_names=['NSGAII', 'SMPSO'])
67+
print("-------- Bayesian Signed Rank Test --------")
68+
bst, DProcess = bayesian_signed_rank_test(
69+
avg[["NSGAII", "SMPSO"]], rope_limits=[-0.002, 0.002], prior_strength=0.5, return_sample=True
70+
)
71+
plot_posterior(DProcess, higher_is_better=True, alg_names=["NSGAII", "SMPSO"])
6572

66-
print('Pr(NSGAII < SMPSO) = %.3f' % bst[0])
67-
print('Pr(NSGAII ~= SMPSO) = %.3f' % bst[1])
68-
print('Pr(NSGAII > SMPSO) = %.3f' % bst[2])
73+
print("Pr(NSGAII < SMPSO) = %.3f" % bst[0])
74+
print("Pr(NSGAII ~= SMPSO) = %.3f" % bst[1])
75+
print("Pr(NSGAII > SMPSO) = %.3f" % bst[2])

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