%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