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@@ -0,0 +1 @@ +/scikit-surprise-1.1.3.tar.gz diff --git a/python-scikit-surprise.spec b/python-scikit-surprise.spec new file mode 100644 index 0000000..cbaca60 --- /dev/null +++ b/python-scikit-surprise.spec @@ -0,0 +1,210 @@ +%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 @@ -0,0 +1 @@ +1b7f4fa05844f8c1b236e0874d0107c2 scikit-surprise-1.1.3.tar.gz |