automatic import of python-scikit-surprise

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+%global _empty_manifest_terminate_build 0
+Name:		python-scikit-surprise
+Version:	1.1.3
+Release:	1
+Summary:	An easy-to-use library for recommender systems.
+License:	GPLv3+
+URL:		https://surpriselib.com
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/30/e1/f4f78b7dd32feaa6256f000668a6932e81b899d0e5a5f84ab3fd1f5e2743/scikit-surprise-1.1.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+[Surprise](https://surpriselib.com) is a Python
+[scikit](https://projects.scipy.org/scikits.html) for building and analyzing
+recommender systems that deal with explicit rating data.
+[Surprise](https://surpriselib.com) **was designed with the
+following purposes in mind**:
+- Give users perfect control over their experiments. To this end, a strong
+  emphasis is laid on
+  [documentation](https://surprise.readthedocs.io/en/stable/index.html), which we
+  have tried to make as clear and precise as possible by pointing out every
+  detail of the algorithms.
+- Alleviate the pain of [Dataset
+  handling](https://surprise.readthedocs.io/en/stable/getting_started.html#load-a-custom-dataset).
+  Users can use both *built-in* datasets
+  ([Movielens](https://grouplens.org/datasets/movielens/),
+  [Jester](https://eigentaste.berkeley.edu/dataset/)), and their own *custom*
+  datasets.
+- Provide various ready-to-use [prediction
+  algorithms](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html)
+  such as [baseline
+  algorithms](https://surprise.readthedocs.io/en/stable/basic_algorithms.html),
+  [neighborhood
+  methods](https://surprise.readthedocs.io/en/stable/knn_inspired.html), matrix
+  factorization-based (
+  [SVD](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD),
+  [PMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#unbiased-note),
+  [SVD++](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp),
+  [NMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)),
+  and [many
+  others](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html).
+  Also, various [similarity
+  measures](https://surprise.readthedocs.io/en/stable/similarities.html)
+  (cosine, MSD, pearson...) are built-in.
+- Make it easy to implement [new algorithm
+  ideas](https://surprise.readthedocs.io/en/stable/building_custom_algo.html).
+- Provide tools to [evaluate](https://surprise.readthedocs.io/en/stable/model_selection.html),
+  [analyse](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/tree/master/examples/notebooks/KNNBasic_analysis.ipynb/)
+  and
+  [compare](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/blob/master/examples/notebooks/Compare.ipynb)
+  the algorithms' performance. Cross-validation procedures can be run very
+  easily using powerful CV iterators (inspired by
+  [scikit-learn](https://scikit-learn.org/) excellent tools), as well as
+  [exhaustive search over a set of
+  parameters](https://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv).
+The name *SurPRISE* (roughly :) ) stands for *Simple Python RecommendatIon
+System Engine*.
+Please note that surprise does not support implicit ratings or content-based
+information.
+
+%package -n python3-scikit-surprise
+Summary:	An easy-to-use library for recommender systems.
+Provides:	python-scikit-surprise
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-scikit-surprise
+[Surprise](https://surpriselib.com) is a Python
+[scikit](https://projects.scipy.org/scikits.html) for building and analyzing
+recommender systems that deal with explicit rating data.
+[Surprise](https://surpriselib.com) **was designed with the
+following purposes in mind**:
+- Give users perfect control over their experiments. To this end, a strong
+  emphasis is laid on
+  [documentation](https://surprise.readthedocs.io/en/stable/index.html), which we
+  have tried to make as clear and precise as possible by pointing out every
+  detail of the algorithms.
+- Alleviate the pain of [Dataset
+  handling](https://surprise.readthedocs.io/en/stable/getting_started.html#load-a-custom-dataset).
+  Users can use both *built-in* datasets
+  ([Movielens](https://grouplens.org/datasets/movielens/),
+  [Jester](https://eigentaste.berkeley.edu/dataset/)), and their own *custom*
+  datasets.
+- Provide various ready-to-use [prediction
+  algorithms](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html)
+  such as [baseline
+  algorithms](https://surprise.readthedocs.io/en/stable/basic_algorithms.html),
+  [neighborhood
+  methods](https://surprise.readthedocs.io/en/stable/knn_inspired.html), matrix
+  factorization-based (
+  [SVD](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD),
+  [PMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#unbiased-note),
+  [SVD++](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp),
+  [NMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)),
+  and [many
+  others](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html).
+  Also, various [similarity
+  measures](https://surprise.readthedocs.io/en/stable/similarities.html)
+  (cosine, MSD, pearson...) are built-in.
+- Make it easy to implement [new algorithm
+  ideas](https://surprise.readthedocs.io/en/stable/building_custom_algo.html).
+- Provide tools to [evaluate](https://surprise.readthedocs.io/en/stable/model_selection.html),
+  [analyse](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/tree/master/examples/notebooks/KNNBasic_analysis.ipynb/)
+  and
+  [compare](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/blob/master/examples/notebooks/Compare.ipynb)
+  the algorithms' performance. Cross-validation procedures can be run very
+  easily using powerful CV iterators (inspired by
+  [scikit-learn](https://scikit-learn.org/) excellent tools), as well as
+  [exhaustive search over a set of
+  parameters](https://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv).
+The name *SurPRISE* (roughly :) ) stands for *Simple Python RecommendatIon
+System Engine*.
+Please note that surprise does not support implicit ratings or content-based
+information.
+
+%package help
+Summary:	Development documents and examples for scikit-surprise
+Provides:	python3-scikit-surprise-doc
+%description help
+[Surprise](https://surpriselib.com) is a Python
+[scikit](https://projects.scipy.org/scikits.html) for building and analyzing
+recommender systems that deal with explicit rating data.
+[Surprise](https://surpriselib.com) **was designed with the
+following purposes in mind**:
+- Give users perfect control over their experiments. To this end, a strong
+  emphasis is laid on
+  [documentation](https://surprise.readthedocs.io/en/stable/index.html), which we
+  have tried to make as clear and precise as possible by pointing out every
+  detail of the algorithms.
+- Alleviate the pain of [Dataset
+  handling](https://surprise.readthedocs.io/en/stable/getting_started.html#load-a-custom-dataset).
+  Users can use both *built-in* datasets
+  ([Movielens](https://grouplens.org/datasets/movielens/),
+  [Jester](https://eigentaste.berkeley.edu/dataset/)), and their own *custom*
+  datasets.
+- Provide various ready-to-use [prediction
+  algorithms](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html)
+  such as [baseline
+  algorithms](https://surprise.readthedocs.io/en/stable/basic_algorithms.html),
+  [neighborhood
+  methods](https://surprise.readthedocs.io/en/stable/knn_inspired.html), matrix
+  factorization-based (
+  [SVD](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVD),
+  [PMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#unbiased-note),
+  [SVD++](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.SVDpp),
+  [NMF](https://surprise.readthedocs.io/en/stable/matrix_factorization.html#surprise.prediction_algorithms.matrix_factorization.NMF)),
+  and [many
+  others](https://surprise.readthedocs.io/en/stable/prediction_algorithms_package.html).
+  Also, various [similarity
+  measures](https://surprise.readthedocs.io/en/stable/similarities.html)
+  (cosine, MSD, pearson...) are built-in.
+- Make it easy to implement [new algorithm
+  ideas](https://surprise.readthedocs.io/en/stable/building_custom_algo.html).
+- Provide tools to [evaluate](https://surprise.readthedocs.io/en/stable/model_selection.html),
+  [analyse](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/tree/master/examples/notebooks/KNNBasic_analysis.ipynb/)
+  and
+  [compare](https://nbviewer.jupyter.org/github/NicolasHug/Surprise/blob/master/examples/notebooks/Compare.ipynb)
+  the algorithms' performance. Cross-validation procedures can be run very
+  easily using powerful CV iterators (inspired by
+  [scikit-learn](https://scikit-learn.org/) excellent tools), as well as
+  [exhaustive search over a set of
+  parameters](https://surprise.readthedocs.io/en/stable/getting_started.html#tune-algorithm-parameters-with-gridsearchcv).
+The name *SurPRISE* (roughly :) ) stands for *Simple Python RecommendatIon
+System Engine*.
+Please note that surprise does not support implicit ratings or content-based
+information.
+
+%prep
+%autosetup -n scikit-surprise-1.1.3
+
+%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-scikit-surprise -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.3-1
+- Package Spec generated