%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 - 0.4.3-1 - Package Spec generated