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@@ -0,0 +1 @@ +/gplearn-0.4.2.tar.gz diff --git a/python-gplearn.spec b/python-gplearn.spec new file mode 100644 index 0000000..96bb13a --- /dev/null +++ b/python-gplearn.spec @@ -0,0 +1,89 @@ +%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 +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.2-1 +- Package Spec generated @@ -0,0 +1 @@ +03877c06d59a7d64de280e68d609c3f0 gplearn-0.4.2.tar.gz |