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|
%global _empty_manifest_terminate_build 0
Name: python-pymoo
Version: 0.6.0.1
Release: 1
Summary: Multi-Objective Optimization in Python
License: Apache License 2.0
URL: https://pymoo.org
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c1/cb/b382ee907d83cfb28c0c364155703395abe54688ffa3e1713fe62d90a7cd/pymoo-0.6.0.1.tar.gz
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-autograd
Requires: python3-cma
Requires: python3-alive-progress
Requires: python3-dill
Requires: python3-Deprecated
%description
Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features
related to multi-objective optimization such as visualization and decision making.
Installation
********************************************************************************
First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.
The official release is always available at PyPi:
pip install -U pymoo
For the current developer version:
git clone https://github.com/anyoptimization/pymoo
cd pymoo
pip install .
Since for speedup, some of the modules are also available compiled, you can double-check
if the compilation worked. When executing the command, be sure not already being in the local pymoo
directory because otherwise not the in site-packages installed version will be used.
python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"
Usage
********************************************************************************
We refer here to our documentation for all the details.
However, for instance, executing NSGA2:
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.visualization.scatter import Scatter
problem = get_problem("zdt1")
algorithm = NSGA2(pop_size=100)
res = minimize(problem,
algorithm,
('n_gen', 200),
seed=1,
verbose=True)
plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, color="red")
plot.show()
A representative run of NSGA2 looks as follows:
|animation|
Citation
********************************************************************************
If you have used our framework for research purposes, you can cite our publication by:
| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 <https://ieeexplore.ieee.org/document/9078759>`_
|
| BibTex:
@ARTICLE{pymoo,
author={J. {Blank} and K. {Deb}},
journal={IEEE Access},
title={pymoo: Multi-Objective Optimization in Python},
year={2020},
volume={8},
number={},
pages={89497-89509},
}
Contact
********************************************************************************
Feel free to contact me if you have any questions:
| `Julian Blank <http://julianblank.com>`_ (blankjul [at] msu.edu)
| Michigan State University
| Computational Optimization and Innovation Laboratory (COIN)
| East Lansing, MI 48824, USA
%package -n python3-pymoo
Summary: Multi-Objective Optimization in Python
Provides: python-pymoo
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-pymoo
Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features
related to multi-objective optimization such as visualization and decision making.
Installation
********************************************************************************
First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.
The official release is always available at PyPi:
pip install -U pymoo
For the current developer version:
git clone https://github.com/anyoptimization/pymoo
cd pymoo
pip install .
Since for speedup, some of the modules are also available compiled, you can double-check
if the compilation worked. When executing the command, be sure not already being in the local pymoo
directory because otherwise not the in site-packages installed version will be used.
python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"
Usage
********************************************************************************
We refer here to our documentation for all the details.
However, for instance, executing NSGA2:
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.visualization.scatter import Scatter
problem = get_problem("zdt1")
algorithm = NSGA2(pop_size=100)
res = minimize(problem,
algorithm,
('n_gen', 200),
seed=1,
verbose=True)
plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, color="red")
plot.show()
A representative run of NSGA2 looks as follows:
|animation|
Citation
********************************************************************************
If you have used our framework for research purposes, you can cite our publication by:
| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 <https://ieeexplore.ieee.org/document/9078759>`_
|
| BibTex:
@ARTICLE{pymoo,
author={J. {Blank} and K. {Deb}},
journal={IEEE Access},
title={pymoo: Multi-Objective Optimization in Python},
year={2020},
volume={8},
number={},
pages={89497-89509},
}
Contact
********************************************************************************
Feel free to contact me if you have any questions:
| `Julian Blank <http://julianblank.com>`_ (blankjul [at] msu.edu)
| Michigan State University
| Computational Optimization and Innovation Laboratory (COIN)
| East Lansing, MI 48824, USA
%package help
Summary: Development documents and examples for pymoo
Provides: python3-pymoo-doc
%description help
Our open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features
related to multi-objective optimization such as visualization and decision making.
Installation
********************************************************************************
First, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.
The official release is always available at PyPi:
pip install -U pymoo
For the current developer version:
git clone https://github.com/anyoptimization/pymoo
cd pymoo
pip install .
Since for speedup, some of the modules are also available compiled, you can double-check
if the compilation worked. When executing the command, be sure not already being in the local pymoo
directory because otherwise not the in site-packages installed version will be used.
python -c "from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"
Usage
********************************************************************************
We refer here to our documentation for all the details.
However, for instance, executing NSGA2:
from pymoo.algorithms.moo.nsga2 import NSGA2
from pymoo.problems import get_problem
from pymoo.optimize import minimize
from pymoo.visualization.scatter import Scatter
problem = get_problem("zdt1")
algorithm = NSGA2(pop_size=100)
res = minimize(problem,
algorithm,
('n_gen', 200),
seed=1,
verbose=True)
plot = Scatter()
plot.add(problem.pareto_front(), plot_type="line", color="black", alpha=0.7)
plot.add(res.F, color="red")
plot.show()
A representative run of NSGA2 looks as follows:
|animation|
Citation
********************************************************************************
If you have used our framework for research purposes, you can cite our publication by:
| `J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567 <https://ieeexplore.ieee.org/document/9078759>`_
|
| BibTex:
@ARTICLE{pymoo,
author={J. {Blank} and K. {Deb}},
journal={IEEE Access},
title={pymoo: Multi-Objective Optimization in Python},
year={2020},
volume={8},
number={},
pages={89497-89509},
}
Contact
********************************************************************************
Feel free to contact me if you have any questions:
| `Julian Blank <http://julianblank.com>`_ (blankjul [at] msu.edu)
| Michigan State University
| Computational Optimization and Innovation Laboratory (COIN)
| East Lansing, MI 48824, USA
%prep
%autosetup -n pymoo-0.6.0.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pymoo -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.0.1-1
- Package Spec generated
|
