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|
%global _empty_manifest_terminate_build 0
Name: python-pyLDAvis
Version: 3.4.0
Release: 1
Summary: Interactive topic model visualization. Port of the R package.
License: BSD-3-Clause
URL: https://github.com/bmabey/pyLDAvis
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c8/87/c25ac784d4da5f63a7d8b4b9a51c931e9392a4984bec631ebb8cf5de8da6/pyLDAvis-3.4.0.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-pandas
Requires: python3-joblib
Requires: python3-jinja2
Requires: python3-numexpr
Requires: python3-funcy
Requires: python3-scikit-learn
Requires: python3-gensim
Requires: python3-setuptools
%description
Python library for interactive topic model visualization.
This is a port of the fabulous `R package <https://github.com/cpsievert/LDAvis>`_ by `Carson Sievert <https://cpsievert.me/>`__ and `Kenny Shirley <http://www.kennyshirley.com/>`__.
**pyLDAvis** is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.
The visualization is intended to be used within an IPython notebook but can also be saved to a stand-alone HTML file for easy sharing.
Note: LDA stands for `latent Dirichlet allocation <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_.
|version status| |build status| |docs|
Installation
~~~~~~~~~~~~~~~~~~~~~~
- Stable version using pip:
pip install pyldavis
- Development version on GitHub
Clone the repository and run ``python setup.py``
Usage
~~~~~~~~~~~~~~~~~~~~~~
The best way to learn how to use **pyLDAvis** is to see it in action.
Check out this `notebook for an overview <http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb>`__.
Refer to the `documentation <https://pyLDAvis.readthedocs.org>`__ for details.
For a concise explanation of the visualization see this
`vignette <http://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf>`__ from the LDAvis R package.
Video demos
~~~~~~~~~~~
Ben Mabey walked through the visualization in this short talk using a Hacker News corpus:
- `Visualizing Topic Models <https://www.youtube.com/watch?v=tGxW2BzC_DU&index=4&list=PLykRMO7ZuHwP5cWnbEmP_mUIVgzd5DZgH>`__
- `Notebook and visualization used in the demo <http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb>`__
- `Slide deck <https://speakerdeck.com/bmabey/visualizing-topic-models>`__
`Carson Sievert <https://cpsievert.me/>`__ created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:
- `Visualizing & Exploring the Twenty Newsgroup Data <http://stat-graphics.org/movies/ldavis.html>`__
More documentation
~~~~~~~~~~~~~~~~~~
To read about the methodology behind pyLDAvis, see `the original
paper <http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf>`__,
which was presented at the `2014 ACL Workshop on Interactive Language
Learning, Visualization, and
Interfaces <http://nlp.stanford.edu/events/illvi2014/>`__ in Baltimore
on June 27, 2014.
%package -n python3-pyLDAvis
Summary: Interactive topic model visualization. Port of the R package.
Provides: python-pyLDAvis
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pyLDAvis
Python library for interactive topic model visualization.
This is a port of the fabulous `R package <https://github.com/cpsievert/LDAvis>`_ by `Carson Sievert <https://cpsievert.me/>`__ and `Kenny Shirley <http://www.kennyshirley.com/>`__.
**pyLDAvis** is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.
The visualization is intended to be used within an IPython notebook but can also be saved to a stand-alone HTML file for easy sharing.
Note: LDA stands for `latent Dirichlet allocation <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_.
|version status| |build status| |docs|
Installation
~~~~~~~~~~~~~~~~~~~~~~
- Stable version using pip:
pip install pyldavis
- Development version on GitHub
Clone the repository and run ``python setup.py``
Usage
~~~~~~~~~~~~~~~~~~~~~~
The best way to learn how to use **pyLDAvis** is to see it in action.
Check out this `notebook for an overview <http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb>`__.
Refer to the `documentation <https://pyLDAvis.readthedocs.org>`__ for details.
For a concise explanation of the visualization see this
`vignette <http://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf>`__ from the LDAvis R package.
Video demos
~~~~~~~~~~~
Ben Mabey walked through the visualization in this short talk using a Hacker News corpus:
- `Visualizing Topic Models <https://www.youtube.com/watch?v=tGxW2BzC_DU&index=4&list=PLykRMO7ZuHwP5cWnbEmP_mUIVgzd5DZgH>`__
- `Notebook and visualization used in the demo <http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb>`__
- `Slide deck <https://speakerdeck.com/bmabey/visualizing-topic-models>`__
`Carson Sievert <https://cpsievert.me/>`__ created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:
- `Visualizing & Exploring the Twenty Newsgroup Data <http://stat-graphics.org/movies/ldavis.html>`__
More documentation
~~~~~~~~~~~~~~~~~~
To read about the methodology behind pyLDAvis, see `the original
paper <http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf>`__,
which was presented at the `2014 ACL Workshop on Interactive Language
Learning, Visualization, and
Interfaces <http://nlp.stanford.edu/events/illvi2014/>`__ in Baltimore
on June 27, 2014.
%package help
Summary: Development documents and examples for pyLDAvis
Provides: python3-pyLDAvis-doc
%description help
Python library for interactive topic model visualization.
This is a port of the fabulous `R package <https://github.com/cpsievert/LDAvis>`_ by `Carson Sievert <https://cpsievert.me/>`__ and `Kenny Shirley <http://www.kennyshirley.com/>`__.
**pyLDAvis** is designed to help users interpret the topics in a topic model that has been fit to a corpus of text data. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization.
The visualization is intended to be used within an IPython notebook but can also be saved to a stand-alone HTML file for easy sharing.
Note: LDA stands for `latent Dirichlet allocation <https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation>`_.
|version status| |build status| |docs|
Installation
~~~~~~~~~~~~~~~~~~~~~~
- Stable version using pip:
pip install pyldavis
- Development version on GitHub
Clone the repository and run ``python setup.py``
Usage
~~~~~~~~~~~~~~~~~~~~~~
The best way to learn how to use **pyLDAvis** is to see it in action.
Check out this `notebook for an overview <http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/pyLDAvis_overview.ipynb>`__.
Refer to the `documentation <https://pyLDAvis.readthedocs.org>`__ for details.
For a concise explanation of the visualization see this
`vignette <http://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf>`__ from the LDAvis R package.
Video demos
~~~~~~~~~~~
Ben Mabey walked through the visualization in this short talk using a Hacker News corpus:
- `Visualizing Topic Models <https://www.youtube.com/watch?v=tGxW2BzC_DU&index=4&list=PLykRMO7ZuHwP5cWnbEmP_mUIVgzd5DZgH>`__
- `Notebook and visualization used in the demo <http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb>`__
- `Slide deck <https://speakerdeck.com/bmabey/visualizing-topic-models>`__
`Carson Sievert <https://cpsievert.me/>`__ created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:
- `Visualizing & Exploring the Twenty Newsgroup Data <http://stat-graphics.org/movies/ldavis.html>`__
More documentation
~~~~~~~~~~~~~~~~~~
To read about the methodology behind pyLDAvis, see `the original
paper <http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf>`__,
which was presented at the `2014 ACL Workshop on Interactive Language
Learning, Visualization, and
Interfaces <http://nlp.stanford.edu/events/illvi2014/>`__ in Baltimore
on June 27, 2014.
%prep
%autosetup -n pyLDAvis-3.4.0
%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-pyLDAvis -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.4.0-1
- Package Spec generated
|
