Skip to main content

Interactive topic model visualization. Port of the R package.

Project description

Python library for interactive topic model visualization. This is a port of the fabulous R package by Carson Sievert and Kenny Shirley.

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.

version status downloads 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.

Video demos

Carson Sievert created a video demoing the R package. The visualization is the same and so it applies equally to pyLDAvis:

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.

History

0.1.0 (2015-05-29)

  • First release on PyPI. Faithful port of R version with IPython support and helper functions for GraphLab & gensim.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyLDAvis-1.0.0.tar.gz (1.9 MB view details)

Uploaded Source

File details

Details for the file pyLDAvis-1.0.0.tar.gz.

File metadata

  • Download URL: pyLDAvis-1.0.0.tar.gz
  • Upload date:
  • Size: 1.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pyLDAvis-1.0.0.tar.gz
Algorithm Hash digest
SHA256 08b201d3702c2bcd284e3e7a2e162d291d2a35b679ec8e2a16c9ce50add537a8
MD5 1de38c80c33129779c41bce5df4735db
BLAKE2b-256 8bc9d35ab9afe57df87f878b3ca754558a8657293f91a9da9f7e95a8d0793a03

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page