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%global _empty_manifest_terminate_build 0
Name: python-tfidf-matcher
Version: 0.3.0
Release: 1
Summary: A small package that enables super-fast TF-IDF based string matching.
License: MIT License
URL: https://github.com/louistsiattalou/tfidf_matcher
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/cc/cd/af378c05b1f199879f2deed7c31abc4175dad73052f08832a4b4752cdbf6/tfidf_matcher-0.3.0.tar.gz
BuildArch: noarch
Requires: python3-scikit-learn
Requires: python3-pandas
%description
This package provides two functions:
- `ngrams()`: Simple ngram generator.
- `matcher()`: Matches a list of strings against a reference corpus.
Does this by:
- Vectorizing the reference corpus using TF-IDF into a
term-document matrix.
- Fitting a K-NearestNeighbours model to the sparse matrix.
- Vectorizing the list of strings to be matched and passing it in
to the KNN model to calculate the cosine distance (the OOTB
`cosine_similarity` function in sklearn is very
memory-inefficient for our use case).
- Some data manipulation to emit `k_matches` closest matches.
%package -n python3-tfidf-matcher
Summary: A small package that enables super-fast TF-IDF based string matching.
Provides: python-tfidf-matcher
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-tfidf-matcher
This package provides two functions:
- `ngrams()`: Simple ngram generator.
- `matcher()`: Matches a list of strings against a reference corpus.
Does this by:
- Vectorizing the reference corpus using TF-IDF into a
term-document matrix.
- Fitting a K-NearestNeighbours model to the sparse matrix.
- Vectorizing the list of strings to be matched and passing it in
to the KNN model to calculate the cosine distance (the OOTB
`cosine_similarity` function in sklearn is very
memory-inefficient for our use case).
- Some data manipulation to emit `k_matches` closest matches.
%package help
Summary: Development documents and examples for tfidf-matcher
Provides: python3-tfidf-matcher-doc
%description help
This package provides two functions:
- `ngrams()`: Simple ngram generator.
- `matcher()`: Matches a list of strings against a reference corpus.
Does this by:
- Vectorizing the reference corpus using TF-IDF into a
term-document matrix.
- Fitting a K-NearestNeighbours model to the sparse matrix.
- Vectorizing the list of strings to be matched and passing it in
to the KNN model to calculate the cosine distance (the OOTB
`cosine_similarity` function in sklearn is very
memory-inefficient for our use case).
- Some data manipulation to emit `k_matches` closest matches.
%prep
%autosetup -n tfidf-matcher-0.3.0
%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-tfidf-matcher -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.0-1
- Package Spec generated
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