%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 - 0.2.8.6-1 - Package Spec generated