%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 * Sun Apr 23 2023 Python_Bot - 1.1.3-1 - Package Spec generated