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https://github.com/bmabey/pyLDAvis
Python library for interactive topic model visualization. Port of the R LDAvis package.
https://github.com/bmabey/pyLDAvis
Last synced: about 2 months ago
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Python library for interactive topic model visualization. Port of the R LDAvis package.
- Host: GitHub
- URL: https://github.com/bmabey/pyLDAvis
- Owner: bmabey
- License: bsd-3-clause
- Created: 2015-04-09T22:48:03.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2024-02-07T20:20:08.000Z (5 months ago)
- Last Synced: 2024-02-08T20:54:16.469Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 6.15 MB
- Stars: 1,760
- Watchers: 53
- Forks: 355
- Open Issues: 76
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Authors: AUTHORS.rst
Lists
- awesome-python-data-science - pyLDAvis
- awesome_quantmetry - pyLDAvis
- awesome-python-data-science - pyLDAvis - Interactive topic model visualization. (Visualization)
- awesome-python-data-science - pyLDAvis
- awesome-python-machine-learning-resources - GitHub - 51% open · ⏱️ 24.03.2021): (模型的可解释性)
- my-awesome-stars - bmabey/pyLDAvis - Python library for interactive topic model visualization. Port of the R LDAvis package. (Jupyter Notebook)
- awesome-topic-models - pyLDAvis - Python library for interactive topic model visualization (Visualizations / Embedding based Topic Models)
README
pyLDAvis
========
Python library for interactive topic model visualization.
This is a port of the fabulous `R package `_ by `Carson Sievert `__ and `Kenny Shirley `__.
.. figure:: http://www.kennyshirley.com/figures/ldavis-pic.png
:alt: LDAvis icon
**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.
.. |version status| image:: https://img.shields.io/pypi/v/pyLDAvis.svg
:target: https://pypi.python.org/pypi/pyLDAvis
.. |build status| image:: https://travis-ci.org/bmabey/pyLDAvis.png?branch=master
:target: https://travis-ci.org/bmabey/pyLDAvis
.. |docs| image:: https://readthedocs.org/projects/pyldavis/badge/?version=latest
:target: https://pyLDAvis.readthedocs.org