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%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 <Python_Bot@openeuler.org> - 0.23-1
- Package Spec generated
|
