%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 `_ by `Carson Sievert `__ and `Kenny Shirley `__. **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 `_. |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 `__. Refer to the `documentation `__ for details. For a concise explanation of the visualization see this `vignette `__ 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 `__ - `Notebook and visualization used in the demo `__ - `Slide deck `__ `Carson Sievert `__ 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 `__ More documentation ~~~~~~~~~~~~~~~~~~ To read about the methodology behind pyLDAvis, see `the original paper `__, which was presented at the `2014 ACL Workshop on Interactive Language Learning, Visualization, and Interfaces `__ 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 `_ by `Carson Sievert `__ and `Kenny Shirley `__. **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 `_. |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 `__. Refer to the `documentation `__ for details. For a concise explanation of the visualization see this `vignette `__ 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 `__ - `Notebook and visualization used in the demo `__ - `Slide deck `__ `Carson Sievert `__ 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 `__ More documentation ~~~~~~~~~~~~~~~~~~ To read about the methodology behind pyLDAvis, see `the original paper `__, which was presented at the `2014 ACL Workshop on Interactive Language Learning, Visualization, and Interfaces `__ 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 `_ by `Carson Sievert `__ and `Kenny Shirley `__. **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 `_. |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 `__. Refer to the `documentation `__ for details. For a concise explanation of the visualization see this `vignette `__ 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 `__ - `Notebook and visualization used in the demo `__ - `Slide deck `__ `Carson Sievert `__ 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 `__ More documentation ~~~~~~~~~~~~~~~~~~ To read about the methodology behind pyLDAvis, see `the original paper `__, which was presented at the `2014 ACL Workshop on Interactive Language Learning, Visualization, and Interfaces `__ 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 - 3.4.0-1 - Package Spec generated