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diff --git a/python-trustedanalytics.spec b/python-trustedanalytics.spec new file mode 100644 index 0000000..bb38f26 --- /dev/null +++ b/python-trustedanalytics.spec @@ -0,0 +1,114 @@ +%global _empty_manifest_terminate_build 0 +Name: python-trustedanalytics +Version: 0.7.3.post20161020785 +Release: 1 +Summary: Trusted Analytics Toolkit +License: Apache 2 +URL: http://trustedanalytics.github.io/atk +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2c/0b/b33df79ccd4565fe5d56cc30f2a251126da9e5cf8c1589923da1ef15ce16/trustedanalytics-0.7.3.post20161020785.tar.gz +BuildArch: noarch + + +%description +trusted analytics + +trusted analytics is a platform that simplifies applying graph analytics and machine learning to big data for superior knowledge +discovery and predictive modeling across a wide variety of use cases and solutions. ATK provides an analytics pipeline +spanning feature engineering, graph construction, graph analytics, and machine learning using an extensible, +modular framework. By unifying graph and entity-based machine learning, machine learning developers can incorporate an +entity's nearby relationships to yield superior predictive models that better represent the contextual information in +the data. All functionality operates at full scale, yet are accessed using a higher level Python data science +programming abstraction to significantly ease the complexity of cluster computing and parallel processing. +The platform is fully extensible through a plugin architecture that allows incorporating the full range of analytics +and machine learning for any solution need in a unified workflow that frees the researchers from the overhead of +understanding, integrating, and inefficiently iterating across a diversity of formats and interfaces. + +Documentation http://trustedanalytics.github.io/atk/ +Source https://github.com/trustedanalytics/atk + +%package -n python3-trustedanalytics +Summary: Trusted Analytics Toolkit +Provides: python-trustedanalytics +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-trustedanalytics +trusted analytics + +trusted analytics is a platform that simplifies applying graph analytics and machine learning to big data for superior knowledge +discovery and predictive modeling across a wide variety of use cases and solutions. ATK provides an analytics pipeline +spanning feature engineering, graph construction, graph analytics, and machine learning using an extensible, +modular framework. By unifying graph and entity-based machine learning, machine learning developers can incorporate an +entity's nearby relationships to yield superior predictive models that better represent the contextual information in +the data. All functionality operates at full scale, yet are accessed using a higher level Python data science +programming abstraction to significantly ease the complexity of cluster computing and parallel processing. +The platform is fully extensible through a plugin architecture that allows incorporating the full range of analytics +and machine learning for any solution need in a unified workflow that frees the researchers from the overhead of +understanding, integrating, and inefficiently iterating across a diversity of formats and interfaces. + +Documentation http://trustedanalytics.github.io/atk/ +Source https://github.com/trustedanalytics/atk + +%package help +Summary: Development documents and examples for trustedanalytics +Provides: python3-trustedanalytics-doc +%description help +trusted analytics + +trusted analytics is a platform that simplifies applying graph analytics and machine learning to big data for superior knowledge +discovery and predictive modeling across a wide variety of use cases and solutions. ATK provides an analytics pipeline +spanning feature engineering, graph construction, graph analytics, and machine learning using an extensible, +modular framework. By unifying graph and entity-based machine learning, machine learning developers can incorporate an +entity's nearby relationships to yield superior predictive models that better represent the contextual information in +the data. All functionality operates at full scale, yet are accessed using a higher level Python data science +programming abstraction to significantly ease the complexity of cluster computing and parallel processing. +The platform is fully extensible through a plugin architecture that allows incorporating the full range of analytics +and machine learning for any solution need in a unified workflow that frees the researchers from the overhead of +understanding, integrating, and inefficiently iterating across a diversity of formats and interfaces. + +Documentation http://trustedanalytics.github.io/atk/ +Source https://github.com/trustedanalytics/atk + +%prep +%autosetup -n trustedanalytics-0.7.3.post20161020785 + +%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-trustedanalytics -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.3.post20161020785-1 +- Package Spec generated |