1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
%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
# <img src="https://raw.githubusercontent.com/HindyDS/RecursiveFeatureSelector/main/logo/RFS%2010.5.2021.png" height="277">
[](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
# <img src="https://raw.githubusercontent.com/HindyDS/RecursiveFeatureSelector/main/logo/RFS%2010.5.2021.png" height="277">
[](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
# <img src="https://raw.githubusercontent.com/HindyDS/RecursiveFeatureSelector/main/logo/RFS%2010.5.2021.png" height="277">
[](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
* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.8-1
- Package Spec generated
|
