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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
|
