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%global _empty_manifest_terminate_build 0
Name: python-Optunity
Version: 1.1.1
Release: 1
Summary: Optimization routines for hyperparameter tuning.
License: LICENSE.txt
URL: http://www.optunity.net
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/32/4d/d49876a49e105b56755eb5ba06a4848ee8010f7ff9e0f11a13aefed12063/Optunity-1.1.1.tar.gz
BuildArch: noarch
%description
Optunity is a library containing various optimizers for hyperparameter tuning.
Hyperparameter tuning is a recurrent problem in many machine learning tasks,
both supervised and unsupervised. Tuning examples include optimizing
regularization or kernel parameters.
From an optimization point of view, the tuning problem can be considered as
follows: the objective function is non-convex, non-differentiable and
typically expensive to evaluate.
This package provides several distinct approaches to solve such problems including
some helpful facilities such as cross-validation and a plethora of score functions.
The Optunity library is implemented in Python and allows straightforward
integration in other machine learning environments, including R and MATLAB.
If you have any comments, suggestions you can get in touch with us at gitter:
To get started with Optunity on Linux, issue the following commands::
git clone https://github.com/claesenm/optunity.git
echo "export PYTHONPATH=$PYTHONPATH:$(pwd)/optunity" >> ~/.bashrc
Afterwards, importing ``optunity`` should work in Python::
#!/usr/bin/env python
import optunity
Optunity is developed at the STADIUS lab of the dept. of electrical engineering
at KU Leuven (ESAT). Optunity is free software, using a BSD license.
For more information, please refer to the following pages:
http://www.optunity.net
%package -n python3-Optunity
Summary: Optimization routines for hyperparameter tuning.
Provides: python-Optunity
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-Optunity
Optunity is a library containing various optimizers for hyperparameter tuning.
Hyperparameter tuning is a recurrent problem in many machine learning tasks,
both supervised and unsupervised. Tuning examples include optimizing
regularization or kernel parameters.
From an optimization point of view, the tuning problem can be considered as
follows: the objective function is non-convex, non-differentiable and
typically expensive to evaluate.
This package provides several distinct approaches to solve such problems including
some helpful facilities such as cross-validation and a plethora of score functions.
The Optunity library is implemented in Python and allows straightforward
integration in other machine learning environments, including R and MATLAB.
If you have any comments, suggestions you can get in touch with us at gitter:
To get started with Optunity on Linux, issue the following commands::
git clone https://github.com/claesenm/optunity.git
echo "export PYTHONPATH=$PYTHONPATH:$(pwd)/optunity" >> ~/.bashrc
Afterwards, importing ``optunity`` should work in Python::
#!/usr/bin/env python
import optunity
Optunity is developed at the STADIUS lab of the dept. of electrical engineering
at KU Leuven (ESAT). Optunity is free software, using a BSD license.
For more information, please refer to the following pages:
http://www.optunity.net
%package help
Summary: Development documents and examples for Optunity
Provides: python3-Optunity-doc
%description help
Optunity is a library containing various optimizers for hyperparameter tuning.
Hyperparameter tuning is a recurrent problem in many machine learning tasks,
both supervised and unsupervised. Tuning examples include optimizing
regularization or kernel parameters.
From an optimization point of view, the tuning problem can be considered as
follows: the objective function is non-convex, non-differentiable and
typically expensive to evaluate.
This package provides several distinct approaches to solve such problems including
some helpful facilities such as cross-validation and a plethora of score functions.
The Optunity library is implemented in Python and allows straightforward
integration in other machine learning environments, including R and MATLAB.
If you have any comments, suggestions you can get in touch with us at gitter:
To get started with Optunity on Linux, issue the following commands::
git clone https://github.com/claesenm/optunity.git
echo "export PYTHONPATH=$PYTHONPATH:$(pwd)/optunity" >> ~/.bashrc
Afterwards, importing ``optunity`` should work in Python::
#!/usr/bin/env python
import optunity
Optunity is developed at the STADIUS lab of the dept. of electrical engineering
at KU Leuven (ESAT). Optunity is free software, using a BSD license.
For more information, please refer to the following pages:
http://www.optunity.net
%prep
%autosetup -n Optunity-1.1.1
%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-Optunity -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
- Package Spec generated
|
