From 80e97902d4163bf9064382bbb1ec7411f7c7e377 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 05:15:55 +0000 Subject: automatic import of python-ioh --- .gitignore | 1 + python-ioh.spec | 312 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 314 insertions(+) create mode 100644 python-ioh.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..43e3f85 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/ioh-0.3.9.tar.gz diff --git a/python-ioh.spec b/python-ioh.spec new file mode 100644 index 0000000..f2eb51a --- /dev/null +++ b/python-ioh.spec @@ -0,0 +1,312 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ioh +Version: 0.3.9 +Release: 1 +Summary: The experimenter for Iterative Optimization Heuristics +License: BSD +URL: https://iohprofiler.github.io/IOHexperimenter +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/80/d9/31320b3dcc2984a17b28202682a03a2ba5e780e93fdb0c247dc260b5d035/ioh-0.3.9.tar.gz + +Requires: python3-numpy + +%description +# IOHexperimenter + +![Ubuntu g++-{10, 9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/Ubuntu/badge.svg) +![MacOS clang++, g++-{9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/MacOS/badge.svg) +![Windows MVSC-2019](https://github.com/IOHprofiler/IOHexperimenter/workflows/Windows/badge.svg) + +**Experimenter** for **I**terative **O**ptimization **H**euristics (IOHs), built in* `C++`. + +* **Documentation**: [https://iohprofiler.github.io/IOHexperimenter](https://iohprofiler.github.io/IOHexperimenter). +* **Publication**: [https://arxiv.org/abs/1810.05281](https://arxiv.org/abs/1810.05281). +* **Wiki page**: [https://iohprofiler.github.io](https://iohprofiler.github.io/). + +**IOHexperimenter** *provides*: + +* A framework to ease the benchmarking of any iterative optimization heuristic. +* [Pseudo-Boolean Optimization (PBO)](https://iohprofiler.github.io/IOHproblem/) problem set (25 pseudo-Boolean problems). +* Integration of the well-known [Black-black Optimization Benchmarking (BBOB)](https://github.com/numbbo/coco) problem set (24 continuous problems). +* [W-model](https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw) problem sets constructed on OneMax and LeadingOnes. +* Integration of the [Tree Decomposition (TD) Mk Landscapes](https://github.com/tobiasvandriessel/problem-generator) problems. +* Submodular optimization problems, as seen in the [GECCO '22 workshop](https://cs.adelaide.edu.au/~optlog/CompetitionESO2022.php). +* Flexible interface for adding new suites and problems. +* Advanced logging module that takes care of registering the data in a seamless manner. +* Data format is compatible with [IOHanalyzer](https://github.com/IOHprofiler/IOHanalyzer). + +**Available Problem Suites:** + +* BBOB (Single Objective Noiseless) (COCO) +* SBOX-COST (COCO) +* StarDiscrepancy +* PBO +* Submodular Graph Problems + +## C++ + +The complete API documentation, can be found [here](https://iohprofiler.github.io/IOHexperimenter/cpp), as well as a Getting-Started guide. In addition to the documentation, some example projects can be found in the [example](./example/) folder of this repository. + +## Python + +The pip-version of IOHexperimenters python interface is available via [pip](https://pypi.org/project/ioh). A tutorial with python in the form of a jupyter notebook can be found in the example folder of [this repository](./example/tutorial.ipynb). +A Getting-Started guide and the full API documentation can be found [here](https://iohprofiler.github.io/IOHexperimenter/python). + +## Contact + +If you have any questions, comments or suggestions, please don't hesitate contacting us . + +### Our team + +* [Jacob de Nobel](https://www.universiteitleiden.nl/en/staffmembers/jacob-de-nobel), *Leiden Institute of Advanced Computer Science*, +* [Furong Ye](https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Diederick Vermetten](https://www.universiteitleiden.nl/en/staffmembers/diederick-vermetten#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Hao Wang](https://www.universiteitleiden.nl/en/staffmembers/hao-wang#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Carola Doerr](http://www-desir.lip6.fr/~doerr/), *CNRS and Sorbonne University*, +* [Thomas Bäck](https://www.universiteitleiden.nl/en/staffmembers/thomas-back#tab-1), *Leiden Institute of Advanced Computer Science*, + +When using IOHprofiler and parts thereof, please kindly cite this work as + +Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck, +*IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics*, arXiv e-prints:2111.04077, 2021. + +```bibtex +@ARTICLE{IOHexperimenter, + author = {Jacob de Nobel and + Furong Ye and + Diederick Vermetten and + Hao Wang and + Carola Doerr and + Thomas B{\"{a}}ck}, + title = {{IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics}}, + journal = {arXiv e-prints:2111.04077}, + archivePrefix = "arXiv", + eprint = {2111.04077}, + year = 2021, + month = Nov, + keywords = {Computer Science - Neural and Evolutionary Computing}, + url = {https://arxiv.org/abs/2111.04077} +} +``` + + + + +%package -n python3-ioh +Summary: The experimenter for Iterative Optimization Heuristics +Provides: python-ioh +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +BuildRequires: python3-cffi +BuildRequires: gcc +BuildRequires: gdb +%description -n python3-ioh +# IOHexperimenter + +![Ubuntu g++-{10, 9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/Ubuntu/badge.svg) +![MacOS clang++, g++-{9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/MacOS/badge.svg) +![Windows MVSC-2019](https://github.com/IOHprofiler/IOHexperimenter/workflows/Windows/badge.svg) + +**Experimenter** for **I**terative **O**ptimization **H**euristics (IOHs), built in* `C++`. + +* **Documentation**: [https://iohprofiler.github.io/IOHexperimenter](https://iohprofiler.github.io/IOHexperimenter). +* **Publication**: [https://arxiv.org/abs/1810.05281](https://arxiv.org/abs/1810.05281). +* **Wiki page**: [https://iohprofiler.github.io](https://iohprofiler.github.io/). + +**IOHexperimenter** *provides*: + +* A framework to ease the benchmarking of any iterative optimization heuristic. +* [Pseudo-Boolean Optimization (PBO)](https://iohprofiler.github.io/IOHproblem/) problem set (25 pseudo-Boolean problems). +* Integration of the well-known [Black-black Optimization Benchmarking (BBOB)](https://github.com/numbbo/coco) problem set (24 continuous problems). +* [W-model](https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw) problem sets constructed on OneMax and LeadingOnes. +* Integration of the [Tree Decomposition (TD) Mk Landscapes](https://github.com/tobiasvandriessel/problem-generator) problems. +* Submodular optimization problems, as seen in the [GECCO '22 workshop](https://cs.adelaide.edu.au/~optlog/CompetitionESO2022.php). +* Flexible interface for adding new suites and problems. +* Advanced logging module that takes care of registering the data in a seamless manner. +* Data format is compatible with [IOHanalyzer](https://github.com/IOHprofiler/IOHanalyzer). + +**Available Problem Suites:** + +* BBOB (Single Objective Noiseless) (COCO) +* SBOX-COST (COCO) +* StarDiscrepancy +* PBO +* Submodular Graph Problems + +## C++ + +The complete API documentation, can be found [here](https://iohprofiler.github.io/IOHexperimenter/cpp), as well as a Getting-Started guide. In addition to the documentation, some example projects can be found in the [example](./example/) folder of this repository. + +## Python + +The pip-version of IOHexperimenters python interface is available via [pip](https://pypi.org/project/ioh). A tutorial with python in the form of a jupyter notebook can be found in the example folder of [this repository](./example/tutorial.ipynb). +A Getting-Started guide and the full API documentation can be found [here](https://iohprofiler.github.io/IOHexperimenter/python). + +## Contact + +If you have any questions, comments or suggestions, please don't hesitate contacting us . + +### Our team + +* [Jacob de Nobel](https://www.universiteitleiden.nl/en/staffmembers/jacob-de-nobel), *Leiden Institute of Advanced Computer Science*, +* [Furong Ye](https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Diederick Vermetten](https://www.universiteitleiden.nl/en/staffmembers/diederick-vermetten#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Hao Wang](https://www.universiteitleiden.nl/en/staffmembers/hao-wang#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Carola Doerr](http://www-desir.lip6.fr/~doerr/), *CNRS and Sorbonne University*, +* [Thomas Bäck](https://www.universiteitleiden.nl/en/staffmembers/thomas-back#tab-1), *Leiden Institute of Advanced Computer Science*, + +When using IOHprofiler and parts thereof, please kindly cite this work as + +Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck, +*IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics*, arXiv e-prints:2111.04077, 2021. + +```bibtex +@ARTICLE{IOHexperimenter, + author = {Jacob de Nobel and + Furong Ye and + Diederick Vermetten and + Hao Wang and + Carola Doerr and + Thomas B{\"{a}}ck}, + title = {{IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics}}, + journal = {arXiv e-prints:2111.04077}, + archivePrefix = "arXiv", + eprint = {2111.04077}, + year = 2021, + month = Nov, + keywords = {Computer Science - Neural and Evolutionary Computing}, + url = {https://arxiv.org/abs/2111.04077} +} +``` + + + + +%package help +Summary: Development documents and examples for ioh +Provides: python3-ioh-doc +%description help +# IOHexperimenter + +![Ubuntu g++-{10, 9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/Ubuntu/badge.svg) +![MacOS clang++, g++-{9, 8}](https://github.com/IOHprofiler/IOHexperimenter/workflows/MacOS/badge.svg) +![Windows MVSC-2019](https://github.com/IOHprofiler/IOHexperimenter/workflows/Windows/badge.svg) + +**Experimenter** for **I**terative **O**ptimization **H**euristics (IOHs), built in* `C++`. + +* **Documentation**: [https://iohprofiler.github.io/IOHexperimenter](https://iohprofiler.github.io/IOHexperimenter). +* **Publication**: [https://arxiv.org/abs/1810.05281](https://arxiv.org/abs/1810.05281). +* **Wiki page**: [https://iohprofiler.github.io](https://iohprofiler.github.io/). + +**IOHexperimenter** *provides*: + +* A framework to ease the benchmarking of any iterative optimization heuristic. +* [Pseudo-Boolean Optimization (PBO)](https://iohprofiler.github.io/IOHproblem/) problem set (25 pseudo-Boolean problems). +* Integration of the well-known [Black-black Optimization Benchmarking (BBOB)](https://github.com/numbbo/coco) problem set (24 continuous problems). +* [W-model](https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw) problem sets constructed on OneMax and LeadingOnes. +* Integration of the [Tree Decomposition (TD) Mk Landscapes](https://github.com/tobiasvandriessel/problem-generator) problems. +* Submodular optimization problems, as seen in the [GECCO '22 workshop](https://cs.adelaide.edu.au/~optlog/CompetitionESO2022.php). +* Flexible interface for adding new suites and problems. +* Advanced logging module that takes care of registering the data in a seamless manner. +* Data format is compatible with [IOHanalyzer](https://github.com/IOHprofiler/IOHanalyzer). + +**Available Problem Suites:** + +* BBOB (Single Objective Noiseless) (COCO) +* SBOX-COST (COCO) +* StarDiscrepancy +* PBO +* Submodular Graph Problems + +## C++ + +The complete API documentation, can be found [here](https://iohprofiler.github.io/IOHexperimenter/cpp), as well as a Getting-Started guide. In addition to the documentation, some example projects can be found in the [example](./example/) folder of this repository. + +## Python + +The pip-version of IOHexperimenters python interface is available via [pip](https://pypi.org/project/ioh). A tutorial with python in the form of a jupyter notebook can be found in the example folder of [this repository](./example/tutorial.ipynb). +A Getting-Started guide and the full API documentation can be found [here](https://iohprofiler.github.io/IOHexperimenter/python). + +## Contact + +If you have any questions, comments or suggestions, please don't hesitate contacting us . + +### Our team + +* [Jacob de Nobel](https://www.universiteitleiden.nl/en/staffmembers/jacob-de-nobel), *Leiden Institute of Advanced Computer Science*, +* [Furong Ye](https://www.universiteitleiden.nl/en/staffmembers/furong-ye#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Diederick Vermetten](https://www.universiteitleiden.nl/en/staffmembers/diederick-vermetten#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Hao Wang](https://www.universiteitleiden.nl/en/staffmembers/hao-wang#tab-1), *Leiden Institute of Advanced Computer Science*, +* [Carola Doerr](http://www-desir.lip6.fr/~doerr/), *CNRS and Sorbonne University*, +* [Thomas Bäck](https://www.universiteitleiden.nl/en/staffmembers/thomas-back#tab-1), *Leiden Institute of Advanced Computer Science*, + +When using IOHprofiler and parts thereof, please kindly cite this work as + +Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr and Thomas Bäck, +*IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics*, arXiv e-prints:2111.04077, 2021. + +```bibtex +@ARTICLE{IOHexperimenter, + author = {Jacob de Nobel and + Furong Ye and + Diederick Vermetten and + Hao Wang and + Carola Doerr and + Thomas B{\"{a}}ck}, + title = {{IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics}}, + journal = {arXiv e-prints:2111.04077}, + archivePrefix = "arXiv", + eprint = {2111.04077}, + year = 2021, + month = Nov, + keywords = {Computer Science - Neural and Evolutionary Computing}, + url = {https://arxiv.org/abs/2111.04077} +} +``` + + + + +%prep +%autosetup -n ioh-0.3.9 + +%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-ioh -f filelist.lst +%dir %{python3_sitearch}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot - 0.3.9-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..ad47138 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +77af6a4bdd989bafbd289cfe57536b47 ioh-0.3.9.tar.gz -- cgit v1.2.3