%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