%global _empty_manifest_terminate_build 0
Name: python-RecursiveFeatureSelector
Version: 1.3.8
Release: 1
Summary: Recursively selecting features for machine learning task.
License: MIT
URL: https://github.com/HindyDS/RecursiveFeatureSelector
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/42/33/3d9bb6f0a502044f13625d8d8a9ae79dbccf9ea004d476ce28753b970afb/RecursiveFeatureSelector-1.3.8.tar.gz
BuildArch: noarch
%description
# 
[](https://github.com/ellerbrock/open-source-badges/)
[](https://badge.fury.io/py/RecursiveFeatureSelector)
[](https://opensource.org/licenses/mit-license.php)
RecursiveFeatureSelector aims to select the best features or the subset of features in machine learning tasks according to corresponding score with other incredible packages like numpy, pandas and sklearn.
This package is inspired by:
PyData DC 2016 | A Practical Guide to Dimensionality Reduction
Vishal Patel
October 8, 2016
- **Examples:** https://github.com/HindyDS/RecursiveFeatureSelector/tree/main/examples
- **Email:** hindy888@hotmail.com
- **Source code:** https://github.com/HindyDS/RecursiveFeatureSelector/tree/main/RecursiveFeatureSelector
- **Bug reports:** https://github.com/HindyDS/RecursiveFeatureSelector/issues
It requires at least six arguments to run:
- estimators: machine learning model
- X (array): features space
- y (array): target
- cv (int): number of folds in a (Stratified)KFold
- scoring (str): see https://scikit-learn.org/stable/modules/model_evaluation.html
Optional arguments:
- max_trial (int): number of trials that you wanted RFS to stop searching
- tolerance (int): how many times RFS can fail to find better subset of features
- least_gain (int): threshold of scoring metrics gain in fraction
- max_feats (int): maximum number of features
- prior (list): starting point for RFS to search, must be corresponds to the columns of X
- exclusions (nested list): if the new selected feature is in one of the particular subpool
(list in the nested list), then the features in that particular subpool with no longer be avalible to form any new subset in the following trials
- n_jobs (int): Number of jobs to run in parallel.
- n_digit (int): Decimal places for scoring
- verbose (int): Level of verbosity of RFS
If you have any ideas for this packge please don't hesitate to bring forward!
%package -n python3-RecursiveFeatureSelector
Summary: Recursively selecting features for machine learning task.
Provides: python-RecursiveFeatureSelector
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-RecursiveFeatureSelector
#
[](https://github.com/ellerbrock/open-source-badges/)
[](https://badge.fury.io/py/RecursiveFeatureSelector)
[](https://opensource.org/licenses/mit-license.php)
RecursiveFeatureSelector aims to select the best features or the subset of features in machine learning tasks according to corresponding score with other incredible packages like numpy, pandas and sklearn.
This package is inspired by:
PyData DC 2016 | A Practical Guide to Dimensionality Reduction
Vishal Patel
October 8, 2016
- **Examples:** https://github.com/HindyDS/RecursiveFeatureSelector/tree/main/examples
- **Email:** hindy888@hotmail.com
- **Source code:** https://github.com/HindyDS/RecursiveFeatureSelector/tree/main/RecursiveFeatureSelector
- **Bug reports:** https://github.com/HindyDS/RecursiveFeatureSelector/issues
It requires at least six arguments to run:
- estimators: machine learning model
- X (array): features space
- y (array): target
- cv (int): number of folds in a (Stratified)KFold
- scoring (str): see https://scikit-learn.org/stable/modules/model_evaluation.html
Optional arguments:
- max_trial (int): number of trials that you wanted RFS to stop searching
- tolerance (int): how many times RFS can fail to find better subset of features
- least_gain (int): threshold of scoring metrics gain in fraction
- max_feats (int): maximum number of features
- prior (list): starting point for RFS to search, must be corresponds to the columns of X
- exclusions (nested list): if the new selected feature is in one of the particular subpool
(list in the nested list), then the features in that particular subpool with no longer be avalible to form any new subset in the following trials
- n_jobs (int): Number of jobs to run in parallel.
- n_digit (int): Decimal places for scoring
- verbose (int): Level of verbosity of RFS
If you have any ideas for this packge please don't hesitate to bring forward!
%package help
Summary: Development documents and examples for RecursiveFeatureSelector
Provides: python3-RecursiveFeatureSelector-doc
%description help
#
[](https://github.com/ellerbrock/open-source-badges/)
[](https://badge.fury.io/py/RecursiveFeatureSelector)
[](https://opensource.org/licenses/mit-license.php)
RecursiveFeatureSelector aims to select the best features or the subset of features in machine learning tasks according to corresponding score with other incredible packages like numpy, pandas and sklearn.
This package is inspired by:
PyData DC 2016 | A Practical Guide to Dimensionality Reduction
Vishal Patel
October 8, 2016
- **Examples:** https://github.com/HindyDS/RecursiveFeatureSelector/tree/main/examples
- **Email:** hindy888@hotmail.com
- **Source code:** https://github.com/HindyDS/RecursiveFeatureSelector/tree/main/RecursiveFeatureSelector
- **Bug reports:** https://github.com/HindyDS/RecursiveFeatureSelector/issues
It requires at least six arguments to run:
- estimators: machine learning model
- X (array): features space
- y (array): target
- cv (int): number of folds in a (Stratified)KFold
- scoring (str): see https://scikit-learn.org/stable/modules/model_evaluation.html
Optional arguments:
- max_trial (int): number of trials that you wanted RFS to stop searching
- tolerance (int): how many times RFS can fail to find better subset of features
- least_gain (int): threshold of scoring metrics gain in fraction
- max_feats (int): maximum number of features
- prior (list): starting point for RFS to search, must be corresponds to the columns of X
- exclusions (nested list): if the new selected feature is in one of the particular subpool
(list in the nested list), then the features in that particular subpool with no longer be avalible to form any new subset in the following trials
- n_jobs (int): Number of jobs to run in parallel.
- n_digit (int): Decimal places for scoring
- verbose (int): Level of verbosity of RFS
If you have any ideas for this packge please don't hesitate to bring forward!
%prep
%autosetup -n RecursiveFeatureSelector-1.3.8
%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-RecursiveFeatureSelector -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 29 2023 Python_Bot - 1.3.8-1
- Package Spec generated