%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 - 0.7.3.post20161020785-1 - Package Spec generated