automatic import of python-easylda

3 files changed, 203 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..eec3f8a 100644
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+++ b/.gitignore
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+/easyLDA-0.2.8.6.tar.gz
diff --git a/python-easylda.spec b/python-easylda.spec
new file mode 100644
index 0000000..b1bd7d6
--- /dev/null
+++ b/python-easylda.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-easyLDA
+Version:	0.2.8.6
+Release:	1
+Summary:	easily bult LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT
+License:	MIT
+URL:		https://github.com/shichaoji/easyLDA
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/02/25/06d3ccf63f834d3b5a6f11e4a35271dbf332d842b43386cd82597593f9c3/easyLDA-0.2.8.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-nltk
+Requires:	python3-gensim
+Requires:	python3-pyLDAvis
+
+%description
+|PyPI version|
+easyLDA is a library that easily build LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+github: https://github.com/shichaoji/easyLDA
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-  If you have a collection of documents, and what to explore the
+   relationship & topics of the docs, easyLDA is a very handy library to
+   use. Simply run the commend and you'll get a trained LDA model with
+   results visualized
+The library pipeline text preprocessing, such as tf-idf, n-grams from Gensim library
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Credit to:
+https://radimrehurek.com/gensim/
+http://pyldavis.readthedocs.io/en/latest/readme.html
+installation
+~~~~~~~~~~~~
+``$ pip install easyLDA``
+usage example
+~~~~~~~~~~~~~
+simple need a text file (.csv) with each row represents a document (a post, comment, short article etc.), with only one column which is the text
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+text file (csv) sample view
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+easy to use, just in a shell window, type: easyLDA, then specify the location of the text document
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+1. then choose how many topics you want the model to fit
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+2. choose the topic contains only single word (1) or can be phases (2/3) as well
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+the program will be starting to train
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-  in shell $ easyLDA
+model result
+~~~~~~~~~~~~
+models folder created by program contains the trained model
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+xx.html file is the interactive visulization of the model result
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+visualization live example
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+http://shichaoji.com/2016/02/04/easylda-live-example/
+static pic result
+~~~~~~~~~~~~~~~~~~~~~~
+
+%package -n python3-easyLDA
+Summary:	easily bult LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT
+Provides:	python-easyLDA
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-easyLDA
+|PyPI version|
+easyLDA is a library that easily build LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+github: https://github.com/shichaoji/easyLDA
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-  If you have a collection of documents, and what to explore the
+   relationship & topics of the docs, easyLDA is a very handy library to
+   use. Simply run the commend and you'll get a trained LDA model with
+   results visualized
+The library pipeline text preprocessing, such as tf-idf, n-grams from Gensim library
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Credit to:
+https://radimrehurek.com/gensim/
+http://pyldavis.readthedocs.io/en/latest/readme.html
+installation
+~~~~~~~~~~~~
+``$ pip install easyLDA``
+usage example
+~~~~~~~~~~~~~
+simple need a text file (.csv) with each row represents a document (a post, comment, short article etc.), with only one column which is the text
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+text file (csv) sample view
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+easy to use, just in a shell window, type: easyLDA, then specify the location of the text document
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+1. then choose how many topics you want the model to fit
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+2. choose the topic contains only single word (1) or can be phases (2/3) as well
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+the program will be starting to train
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-  in shell $ easyLDA
+model result
+~~~~~~~~~~~~
+models folder created by program contains the trained model
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+xx.html file is the interactive visulization of the model result
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+visualization live example
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+http://shichaoji.com/2016/02/04/easylda-live-example/
+static pic result
+~~~~~~~~~~~~~~~~~~~~~~
+
+%package help
+Summary:	Development documents and examples for easyLDA
+Provides:	python3-easyLDA-doc
+%description help
+|PyPI version|
+easyLDA is a library that easily build LDA Topic Models with just a list of docs (e.g. a list of twitter posts in CSV/TXT)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+github: https://github.com/shichaoji/easyLDA
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-  If you have a collection of documents, and what to explore the
+   relationship & topics of the docs, easyLDA is a very handy library to
+   use. Simply run the commend and you'll get a trained LDA model with
+   results visualized
+The library pipeline text preprocessing, such as tf-idf, n-grams from Gensim library
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Credit to:
+https://radimrehurek.com/gensim/
+http://pyldavis.readthedocs.io/en/latest/readme.html
+installation
+~~~~~~~~~~~~
+``$ pip install easyLDA``
+usage example
+~~~~~~~~~~~~~
+simple need a text file (.csv) with each row represents a document (a post, comment, short article etc.), with only one column which is the text
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+text file (csv) sample view
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+easy to use, just in a shell window, type: easyLDA, then specify the location of the text document
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+1. then choose how many topics you want the model to fit
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+2. choose the topic contains only single word (1) or can be phases (2/3) as well
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+the program will be starting to train
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-  in shell $ easyLDA
+model result
+~~~~~~~~~~~~
+models folder created by program contains the trained model
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+xx.html file is the interactive visulization of the model result
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+visualization live example
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+http://shichaoji.com/2016/02/04/easylda-live-example/
+static pic result
+~~~~~~~~~~~~~~~~~~~~~~
+
+%prep
+%autosetup -n easyLDA-0.2.8.6
+
+%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-easyLDA -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.8.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..cc13e86
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+cf5840cb4c48f164e5cd6bb2b311e791  easyLDA-0.2.8.6.tar.gz