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%global _empty_manifest_terminate_build 0
Name: python-gplearn
Version: 0.4.2
Release: 1
Summary: Genetic Programming in Python, with a scikit-learn inspired API
License: new BSD
URL: https://github.com/trevorstephens/gplearn
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/91/2d/0a30cb1f4b50865484041e691fe83cffaf64f0da631c42b56178218e4c94/gplearn-0.4.2.tar.gz
BuildArch: noarch
Requires: python3-scikit-learn
Requires: python3-joblib
%description
`gplearn` implements Genetic Programming in Python, with a `scikit-learn <http://scikit-learn.org>`_ inspired and compatible API.
While Genetic Programming (GP) can be used to perform a `very wide variety of tasks <http://www.genetic-programming.org/combined.php>`_, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.
Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.
gplearn retains the familiar scikit-learn `fit/predict` API and works with the existing scikit-learn `pipeline <https://scikit-learn.org/stable/modules/compose.html>`_ and `grid search <http://scikit-learn.org/stable/modules/grid_search.html>`_ modules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, `reading the documentation <http://gplearn.readthedocs.io/>`_ should make the more relevant ones clear for your problem.
gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.
gplearn is built on scikit-learn and a fairly recent copy (1.0.2+) is required for `installation <http://gplearn.readthedocs.io/en/stable/installation.html>`_. If you come across any issues in running or installing the package, `please submit a bug report <https://github.com/trevorstephens/gplearn/issues>`_.
%package -n python3-gplearn
Summary: Genetic Programming in Python, with a scikit-learn inspired API
Provides: python-gplearn
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-gplearn
`gplearn` implements Genetic Programming in Python, with a `scikit-learn <http://scikit-learn.org>`_ inspired and compatible API.
While Genetic Programming (GP) can be used to perform a `very wide variety of tasks <http://www.genetic-programming.org/combined.php>`_, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.
Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.
gplearn retains the familiar scikit-learn `fit/predict` API and works with the existing scikit-learn `pipeline <https://scikit-learn.org/stable/modules/compose.html>`_ and `grid search <http://scikit-learn.org/stable/modules/grid_search.html>`_ modules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, `reading the documentation <http://gplearn.readthedocs.io/>`_ should make the more relevant ones clear for your problem.
gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.
gplearn is built on scikit-learn and a fairly recent copy (1.0.2+) is required for `installation <http://gplearn.readthedocs.io/en/stable/installation.html>`_. If you come across any issues in running or installing the package, `please submit a bug report <https://github.com/trevorstephens/gplearn/issues>`_.
%package help
Summary: Development documents and examples for gplearn
Provides: python3-gplearn-doc
%description help
`gplearn` implements Genetic Programming in Python, with a `scikit-learn <http://scikit-learn.org>`_ inspired and compatible API.
While Genetic Programming (GP) can be used to perform a `very wide variety of tasks <http://www.genetic-programming.org/combined.php>`_, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.
Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.
gplearn retains the familiar scikit-learn `fit/predict` API and works with the existing scikit-learn `pipeline <https://scikit-learn.org/stable/modules/compose.html>`_ and `grid search <http://scikit-learn.org/stable/modules/grid_search.html>`_ modules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, `reading the documentation <http://gplearn.readthedocs.io/>`_ should make the more relevant ones clear for your problem.
gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.
gplearn is built on scikit-learn and a fairly recent copy (1.0.2+) is required for `installation <http://gplearn.readthedocs.io/en/stable/installation.html>`_. If you come across any issues in running or installing the package, `please submit a bug report <https://github.com/trevorstephens/gplearn/issues>`_.
%prep
%autosetup -n gplearn-0.4.2
%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-gplearn -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.2-1
- Package Spec generated
|
