automatic import of python-caserecommenderopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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+/CaseRecommender-1.1.1.tar.gz
diff --git a/python-caserecommender.spec b/python-caserecommender.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-CaseRecommender
+Version:	1.1.1
+Release:	1
+Summary:	A recommender systems framework for Python
+License:	MIT License
+URL:		https://github.com/caserec/CaseRecommender
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4d/2c/c3652c9da575c89fc8c5f8d281d6e1997a09376ee3c22f1f4581fe5d08a2/CaseRecommender-1.1.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-pandas
+
+%description
+Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and
+explicit feedback.  The framework aims to provide a rich set of components from which you can construct a customized
+recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating
+prediction approaches, and different metrics validation and evaluation.
+Algorithms
+^^^^^^^^^^^^
+Item Recommendation:
+- BPRMF
+- ItemKNN
+- Item Attribute KNN
+- UserKNN
+- User Attribute KNN
+- Group-based (Clustering-based algorithm)
+- Paco Recommender (Co-Clustering-based algorithm)
+- Most Popular
+- Random
+- Content Based
+Rating Prediction:
+- Matrix Factorization (with and without baseline)
+- SVD
+- Non-negative Matrix Factorization
+- SVD++
+- ItemKNN
+- Item Attribute KNN
+- UserKNN
+- User Attribute KNN
+- Item NSVD1 (with and without Batch)
+- User NSVD1 (with and without Batch)
+- Most Popular
+- Random
+- gSVD++
+- Item-MSMF
+- (E)CoRec
+Clustering:
+- PaCo: EntroPy Anomalies in Co-Clustering
+- k-medoids
+Evaluation and Validation Metrics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- All-but-one Protocol
+- Cross-fold- Validation
+- Item Recommendation: Precision, Recall, NDCG and Map
+- Rating Prediction: MAE and RMSE
+- Statistical Analysis (T-test and Wilcoxon)
+Requirements
+^^^^^^^^^^^^^
+- Python >= 3
+- scipy
+- numpy
+- pandas
+- scikit-learn
+For Linux, Windows and MAC use:
+    $ pip install requirements
+For Windows libraries help use:
+    http://www.lfd.uci.edu/~gohlke/pythonlibs/
+Quick Start and Guide
+^^^^^^^^^^^^^^^^^^^^^^
+For more information about RiVal and the documentation, 
+visit the Case Recommender 
+`Wiki <https://github.com/caserec/CaseRecommender/wiki>`_. If you have not used Case Recommender before, do check out the Getting Started guide.
+Installation
+^^^^^^^^^^^^^
+Case Recommender can be installed using pip:
+    $ pip install caserecommender
+If you want to run the latest version of the code, you can install from git:
+    $ pip install -U git+git://github.com/caserec/CaseRecommender.git
+More Details
+^^^^^^^^^^^^^
+    `https://github.com/caserec/CaseRecommender <https://github.com/caserec/CaseRecommender>`_
+License (MIT)
+^^^^^^^^^^^^^^
+    © 2019. Case Recommender All Rights Reserved
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+    documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to the following conditions:
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions
+    of the Software.
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+    TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+    THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+    OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS IN THE SOFTWARE.
+
+%package -n python3-CaseRecommender
+Summary:	A recommender systems framework for Python
+Provides:	python-CaseRecommender
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-CaseRecommender
+Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and
+explicit feedback.  The framework aims to provide a rich set of components from which you can construct a customized
+recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating
+prediction approaches, and different metrics validation and evaluation.
+Algorithms
+^^^^^^^^^^^^
+Item Recommendation:
+- BPRMF
+- ItemKNN
+- Item Attribute KNN
+- UserKNN
+- User Attribute KNN
+- Group-based (Clustering-based algorithm)
+- Paco Recommender (Co-Clustering-based algorithm)
+- Most Popular
+- Random
+- Content Based
+Rating Prediction:
+- Matrix Factorization (with and without baseline)
+- SVD
+- Non-negative Matrix Factorization
+- SVD++
+- ItemKNN
+- Item Attribute KNN
+- UserKNN
+- User Attribute KNN
+- Item NSVD1 (with and without Batch)
+- User NSVD1 (with and without Batch)
+- Most Popular
+- Random
+- gSVD++
+- Item-MSMF
+- (E)CoRec
+Clustering:
+- PaCo: EntroPy Anomalies in Co-Clustering
+- k-medoids
+Evaluation and Validation Metrics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- All-but-one Protocol
+- Cross-fold- Validation
+- Item Recommendation: Precision, Recall, NDCG and Map
+- Rating Prediction: MAE and RMSE
+- Statistical Analysis (T-test and Wilcoxon)
+Requirements
+^^^^^^^^^^^^^
+- Python >= 3
+- scipy
+- numpy
+- pandas
+- scikit-learn
+For Linux, Windows and MAC use:
+    $ pip install requirements
+For Windows libraries help use:
+    http://www.lfd.uci.edu/~gohlke/pythonlibs/
+Quick Start and Guide
+^^^^^^^^^^^^^^^^^^^^^^
+For more information about RiVal and the documentation, 
+visit the Case Recommender 
+`Wiki <https://github.com/caserec/CaseRecommender/wiki>`_. If you have not used Case Recommender before, do check out the Getting Started guide.
+Installation
+^^^^^^^^^^^^^
+Case Recommender can be installed using pip:
+    $ pip install caserecommender
+If you want to run the latest version of the code, you can install from git:
+    $ pip install -U git+git://github.com/caserec/CaseRecommender.git
+More Details
+^^^^^^^^^^^^^
+    `https://github.com/caserec/CaseRecommender <https://github.com/caserec/CaseRecommender>`_
+License (MIT)
+^^^^^^^^^^^^^^
+    © 2019. Case Recommender All Rights Reserved
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+    documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to the following conditions:
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions
+    of the Software.
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+    TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+    THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+    OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS IN THE SOFTWARE.
+
+%package help
+Summary:	Development documents and examples for CaseRecommender
+Provides:	python3-CaseRecommender-doc
+%description help
+Case Recommender is a Python implementation of a number of popular recommendation algorithms for both implicit and
+explicit feedback.  The framework aims to provide a rich set of components from which you can construct a customized
+recommender system from a set of algorithms. Case Recommender has different types of item recommendation and rating
+prediction approaches, and different metrics validation and evaluation.
+Algorithms
+^^^^^^^^^^^^
+Item Recommendation:
+- BPRMF
+- ItemKNN
+- Item Attribute KNN
+- UserKNN
+- User Attribute KNN
+- Group-based (Clustering-based algorithm)
+- Paco Recommender (Co-Clustering-based algorithm)
+- Most Popular
+- Random
+- Content Based
+Rating Prediction:
+- Matrix Factorization (with and without baseline)
+- SVD
+- Non-negative Matrix Factorization
+- SVD++
+- ItemKNN
+- Item Attribute KNN
+- UserKNN
+- User Attribute KNN
+- Item NSVD1 (with and without Batch)
+- User NSVD1 (with and without Batch)
+- Most Popular
+- Random
+- gSVD++
+- Item-MSMF
+- (E)CoRec
+Clustering:
+- PaCo: EntroPy Anomalies in Co-Clustering
+- k-medoids
+Evaluation and Validation Metrics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- All-but-one Protocol
+- Cross-fold- Validation
+- Item Recommendation: Precision, Recall, NDCG and Map
+- Rating Prediction: MAE and RMSE
+- Statistical Analysis (T-test and Wilcoxon)
+Requirements
+^^^^^^^^^^^^^
+- Python >= 3
+- scipy
+- numpy
+- pandas
+- scikit-learn
+For Linux, Windows and MAC use:
+    $ pip install requirements
+For Windows libraries help use:
+    http://www.lfd.uci.edu/~gohlke/pythonlibs/
+Quick Start and Guide
+^^^^^^^^^^^^^^^^^^^^^^
+For more information about RiVal and the documentation, 
+visit the Case Recommender 
+`Wiki <https://github.com/caserec/CaseRecommender/wiki>`_. If you have not used Case Recommender before, do check out the Getting Started guide.
+Installation
+^^^^^^^^^^^^^
+Case Recommender can be installed using pip:
+    $ pip install caserecommender
+If you want to run the latest version of the code, you can install from git:
+    $ pip install -U git+git://github.com/caserec/CaseRecommender.git
+More Details
+^^^^^^^^^^^^^
+    `https://github.com/caserec/CaseRecommender <https://github.com/caserec/CaseRecommender>`_
+License (MIT)
+^^^^^^^^^^^^^^
+    © 2019. Case Recommender All Rights Reserved
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+    documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to the following conditions:
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions
+    of the Software.
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
+    TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+    THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+    OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS IN THE SOFTWARE.
+
+%prep
+%autosetup -n CaseRecommender-1.1.1
+
+%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-CaseRecommender -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..968409b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+70e1bf6ae8edd0b3d6d1a83a0ef11cf4  CaseRecommender-1.1.1.tar.gz