%global _empty_manifest_terminate_build 0 Name: python-pyCausalFS Version: 0.23 Release: 1 Summary: Fork of pyCausalFS - implementation of local structure learning algorithms License: GNU General Public License v3 (GPLv3) URL: https://github.com/chris-tran-16/pyCausalFS Source0: https://mirrors.aliyun.com/pypi/web/packages/e7/27/7d58ce91bf80334f02ccc7d0aae32c96bcbd28a3ea3f3f1f175ee25b9ff8/pyCausalFS-0.23.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-scipy Requires: python3-scikit-learn Requires: python3-networkx Requires: python3-matplotlib %description # pyCausalFS：A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification ## Overview This is a fork of pyCausalFS that removes example files and data to allow for easier integration as a python module. The original repository can be found here: https://github.com/wt-hu/pyCausalFS. This fork may have tweaks to make it easier to pip install. You can install this via pypi: pip install pyCausalFS The pyCausalFS library provides access to a wide range of well-established and state-of-the-art causality-based feature selection approaches. The library is designed to facilitate the development of new algorithms in this research area and make it easier to compare new methods and existing ones available. The pyCausalFS library implements 30 representative causality-based feature selection methods. Specifically, it consists of 25 methods using conditional independence tests (16 single MB learning algorithms, 3 multiple MB learning algorithms, and 6 PC learning algorithms), and 5 score-based approaches. 1） Constraint-based MB learning methods: GSMB, IAMB, IAMBnPC, Inter-IAMB, Fast-IAMB, Inter-IAMBnPC, LRH, BAMB, FBEDk, MMMB, PCMB, HITON-MB, Semi-HITON-MB, IPCMB, STMB, MBOR 2） Multiple MB learning methods: KIAMB, TIE*(TIE and TIE_p) 3） Constraint-based PC learning methods: PC-simple, MBtoPC, HITON-PC, Semi-HITON-PC, GetPC, MMPC 4） score-based MB learning methods: SLL, S^2TMB, S^2TMB_p 5） score-based PC learning methods: SLL-PC, S^2TMB-PC Furthermore, using the pyCausalFS library, users can easily generate different local structure learning methods and local-to-global structure learning methods, which includes 3 local BN structure learning algorithms and three local-to-global BN learning algorithms. 6） local BN structure learning algorithms: PCD-by-PCD, MB-by-MB, CMB 7） local-to-global BN learning algorithms: MMHC, GSBN, MBGSL All implementation details please read the manual documentation. %package -n python3-pyCausalFS Summary: Fork of pyCausalFS - implementation of local structure learning algorithms Provides: python-pyCausalFS BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pyCausalFS # pyCausalFS：A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification ## Overview This is a fork of pyCausalFS that removes example files and data to allow for easier integration as a python module. The original repository can be found here: https://github.com/wt-hu/pyCausalFS. This fork may have tweaks to make it easier to pip install. You can install this via pypi: pip install pyCausalFS The pyCausalFS library provides access to a wide range of well-established and state-of-the-art causality-based feature selection approaches. The library is designed to facilitate the development of new algorithms in this research area and make it easier to compare new methods and existing ones available. The pyCausalFS library implements 30 representative causality-based feature selection methods. Specifically, it consists of 25 methods using conditional independence tests (16 single MB learning algorithms, 3 multiple MB learning algorithms, and 6 PC learning algorithms), and 5 score-based approaches. 1） Constraint-based MB learning methods: GSMB, IAMB, IAMBnPC, Inter-IAMB, Fast-IAMB, Inter-IAMBnPC, LRH, BAMB, FBEDk, MMMB, PCMB, HITON-MB, Semi-HITON-MB, IPCMB, STMB, MBOR 2） Multiple MB learning methods: KIAMB, TIE*(TIE and TIE_p) 3） Constraint-based PC learning methods: PC-simple, MBtoPC, HITON-PC, Semi-HITON-PC, GetPC, MMPC 4） score-based MB learning methods: SLL, S^2TMB, S^2TMB_p 5） score-based PC learning methods: SLL-PC, S^2TMB-PC Furthermore, using the pyCausalFS library, users can easily generate different local structure learning methods and local-to-global structure learning methods, which includes 3 local BN structure learning algorithms and three local-to-global BN learning algorithms. 6） local BN structure learning algorithms: PCD-by-PCD, MB-by-MB, CMB 7） local-to-global BN learning algorithms: MMHC, GSBN, MBGSL All implementation details please read the manual documentation. %package help Summary: Development documents and examples for pyCausalFS Provides: python3-pyCausalFS-doc %description help # pyCausalFS：A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification ## Overview This is a fork of pyCausalFS that removes example files and data to allow for easier integration as a python module. The original repository can be found here: https://github.com/wt-hu/pyCausalFS. This fork may have tweaks to make it easier to pip install. You can install this via pypi: pip install pyCausalFS The pyCausalFS library provides access to a wide range of well-established and state-of-the-art causality-based feature selection approaches. The library is designed to facilitate the development of new algorithms in this research area and make it easier to compare new methods and existing ones available. The pyCausalFS library implements 30 representative causality-based feature selection methods. Specifically, it consists of 25 methods using conditional independence tests (16 single MB learning algorithms, 3 multiple MB learning algorithms, and 6 PC learning algorithms), and 5 score-based approaches. 1） Constraint-based MB learning methods: GSMB, IAMB, IAMBnPC, Inter-IAMB, Fast-IAMB, Inter-IAMBnPC, LRH, BAMB, FBEDk, MMMB, PCMB, HITON-MB, Semi-HITON-MB, IPCMB, STMB, MBOR 2） Multiple MB learning methods: KIAMB, TIE*(TIE and TIE_p) 3） Constraint-based PC learning methods: PC-simple, MBtoPC, HITON-PC, Semi-HITON-PC, GetPC, MMPC 4） score-based MB learning methods: SLL, S^2TMB, S^2TMB_p 5） score-based PC learning methods: SLL-PC, S^2TMB-PC Furthermore, using the pyCausalFS library, users can easily generate different local structure learning methods and local-to-global structure learning methods, which includes 3 local BN structure learning algorithms and three local-to-global BN learning algorithms. 6） local BN structure learning algorithms: PCD-by-PCD, MB-by-MB, CMB 7） local-to-global BN learning algorithms: MMHC, GSBN, MBGSL All implementation details please read the manual documentation. %prep %autosetup -n pyCausalFS-0.23 %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-pyCausalFS -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 0.23-1 - Package Spec generated