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%global _empty_manifest_terminate_build 0
Name: python-structureboost
Version: 0.4.3
Release: 1
Summary: StructureBoost is a Python package for gradient boosting using categorical structure. See documentation at: https://structureboost.readthedocs.io/
License: MIT
URL: https://github.com/numeristical/structureboost
Source0: https://mirrors.aliyun.com/pypi/web/packages/d5/a2/7375ca57d807136eda7ff601d310c96d8cf9ab6fd146165809b546de8d64/structureboost-0.4.3.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-matplotlib
Requires: python3-joblib
Requires: python3-ml-insights
%description
# StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the **structure** of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some [examples](https://github.com/numeristical/structureboost/tree/master/examples)
## Video Lectures
There are some explanatory videos on the [Numeristical Youtube Channel](https://www.youtube.com/channel/UCfsbASar8nsLs4NbhQwuaVg)
## Documentation
[Read the Docs](https://structureboost.readthedocs.io/)
## References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. [http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf](http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf)
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." [https://arxiv.org/abs/2007.04446](https://arxiv.org/abs/2007.04446)
%package -n python3-structureboost
Summary: StructureBoost is a Python package for gradient boosting using categorical structure. See documentation at: https://structureboost.readthedocs.io/
Provides: python-structureboost
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-structureboost
# StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the **structure** of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some [examples](https://github.com/numeristical/structureboost/tree/master/examples)
## Video Lectures
There are some explanatory videos on the [Numeristical Youtube Channel](https://www.youtube.com/channel/UCfsbASar8nsLs4NbhQwuaVg)
## Documentation
[Read the Docs](https://structureboost.readthedocs.io/)
## References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. [http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf](http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf)
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." [https://arxiv.org/abs/2007.04446](https://arxiv.org/abs/2007.04446)
%package help
Summary: Development documents and examples for structureboost
Provides: python3-structureboost-doc
%description help
# StructureBoost
StructureBoost is a package to do Gradient Boosting in a manner that exploits the **structure** of categorical variables.
Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"
Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)
StructureBoost can help. Read the documentation and references below. Or dive into some [examples](https://github.com/numeristical/structureboost/tree/master/examples)
## Video Lectures
There are some explanatory videos on the [Numeristical Youtube Channel](https://www.youtube.com/channel/UCfsbASar8nsLs4NbhQwuaVg)
## Documentation
[Read the Docs](https://structureboost.readthedocs.io/)
## References:
Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. [http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf](http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf)
Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." [https://arxiv.org/abs/2007.04446](https://arxiv.org/abs/2007.04446)
%prep
%autosetup -n structureboost-0.4.3
%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-structureboost -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.3-1
- Package Spec generated
|
