ugtm

ugtm: a Python package for Generative Topographic Mapping

https://github.com/hagax8/ugtm

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (12.5%) to scientific vocabulary

Keywords

algorithm classification gtm machine-learning machine-learning-algorithms python regression
Last synced: 6 months ago · JSON representation

Repository

ugtm: a Python package for Generative Topographic Mapping

Basic Info
Statistics
  • Stars: 51
  • Watchers: 4
  • Forks: 9
  • Open Issues: 4
  • Releases: 0
Topics
algorithm classification gtm machine-learning machine-learning-algorithms python regression
Created over 8 years ago · Last pushed almost 5 years ago
Metadata Files
Readme Changelog License

README.md

ugtm: Generative Topographic Mapping with Python.

Link to the package documentation: http://ugtm.readthedocs.io/en/latest/

GTM (Generative Topographic Mapping) is a dimensionality reduction algorithm (as t-SNE, LLE, etc) created by Bishop et al. (https://www.microsoft.com/en-us/research/wp-content/uploads/1998/01/bishop-gtm-ncomp-98.pdf) and a probabilistic counterpart of Kohonen maps.

ugtm is a python package implementing GTM and GTM prediction algorithms. ugtm contains the core functions and runGTM.py (in bin directory) is an easy-to-use program. The kernel version of the algorithm (kGTM) is also implemented. You can also generate regression or classification maps, or evaluate the predictive accuracy (classification) or RMSE/R2 (regression) in repeated cross-validation experiments.

Install ugtm

Simple installation: - pip install ugtm

If you get error messages, try upgrading packages: - pip install --upgrade pip numpy scikit-learn matplotlib scipy mpld3 jinja2 - sudo pip install --upgrade pip numpy scikit-learn matplotlib scipy mpld3 jinja2

If you have problems with anaconda packages, try to create a virtual env called "p2" for python 2.7.14: - conda create -n p2 python=2.7.14 numpy=1.14.5 scikit-learn=0.20 matplotlib=2.2.2 scipy=0.19.1 mpld3=0.3 jinja2=2.10 - source activate p2 - pip install ugtm

Or p3 for python 3.6.6: - conda create -n p3 python=3.6.6 numpy=1.14.5 scikit-learn=0.20 matplotlib=2.2.2 scipy=0.19.1 mpld3=0.3 jinja2=2.10 - source activate p3 - pip install ugtm

Documentation

Readthedocs

Prerequisites

Python 2.7 or + (tested on Python 3.4.6 and Python 2.7.14)

and following packages: - scikit-learn>=0.20 - numpy>=1.14.5 - matplotlib>=2.2.2 - scipy>=0.19.1 - mpld3>=0.3 - jinja2>=2.10

Citing ugtm

Cite ugtm version and the following paper:

@ARTICLE{Gaspar2018-qt, title = "ugtm: A Python Package for Data Modeling and Visualization Using Generative Topographic Mapping", author = "Gaspar, H{\'e}l{\'e}na Alexandra", journal = "Journal of Open Research Software", volume = 6, pages = "215", month = dec, year = 2018 }

Principal author / admin

Héléna A. Gaspar, hagax8@gmail.com, https://github.com/hagax8

Owner

  • Name: Héléna A. Gaspar
  • Login: hagax8
  • Kind: user
  • Location: London

Cheminformatician + musician. PhD in Cheminformatics from the Université de Strasbourg.

GitHub Events

Total
  • Watch event: 5
Last Year
  • Watch event: 5

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 129
  • Total Committers: 9
  • Avg Commits per committer: 14.333
  • Development Distribution Score (DDS): 0.403
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Héléna A. Gaspar h****8@g****m 77
git g****t@g****m 36
EngineerCoding E****g@h****l 4
Héléna Gaspar h****r@b****i 4
hagax8 k****6@l****e 3
sshojiro s****a@g****m 2
EngineerCoding E****g@h****l 1
Héléna Gaspar h****8@M****l 1
Philippe Rivière f****l@r****t 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 7
  • Total pull requests: 7
  • Average time to close issues: 13 days
  • Average time to close pull requests: 19 days
  • Total issue authors: 5
  • Total pull request authors: 4
  • Average comments per issue: 1.0
  • Average comments per pull request: 1.0
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • hagax8 (3)
  • UnixJunkie (1)
  • kirtov (1)
  • EngineerCoding (1)
  • stsouko (1)
Pull Request Authors
  • EngineerCoding (3)
  • hagax8 (2)
  • sshojiro (1)
  • Fil (1)
Top Labels
Issue Labels
enhancement (2) wontfix (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 121 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 7
  • Total maintainers: 1
pypi.org: ugtm

Generative Topographic Mapping (GTM) for python, GTM classification and GTM regression

  • Versions: 7
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 121 Last month
Rankings
Dependent packages count: 10.1%
Stargazers count: 10.2%
Forks count: 11.9%
Average: 16.2%
Dependent repos count: 21.6%
Downloads: 27.1%
Maintainers (1)
Last synced: 6 months ago

Dependencies

docs/source/requires.txt pypi
  • altair >=2.2.2
  • contextlib2 *
  • docutils *
  • jinja2 >=2.10
  • matplotlib >=2.2.2
  • mpld3 >=0.3
  • numpy >=1.14.5
  • scikit-learn >=0.20
  • scipy >=0.19.1
setup.py pypi
  • jinja2 >=2.10.0
  • matplotlib >=2.2.2
  • mpld3 >=0.3
  • numpy >=1.14.5
  • scikit-learn >=0.20.0
  • scipy >=0.19.1
ugtm.egg-info/requires.txt pypi
  • altair >=2.2.2
  • contextlib2 *
  • jinja2 >=2.10.0
  • matplotlib >=2.2.2
  • mpld3 >=0.3
  • numpy >=1.14.5
  • scikit-learn >=0.20.0
  • scipy >=0.19.1