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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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.8.6-1
- Package Spec generated
|
