Data

Tools

Indexes

Applications

Experiments

Open Source Science

paradime

A framework for parametric dimensionality reduction

https://github.com/jku-vds-lab/paradime

Science Score: 52.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ✓
    Institutional organization owner
    Organization jku-vds-lab has institutional domain (jku-vds-lab.at)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.5%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

A framework for parametric dimensionality reduction

Basic Info
  • Host: GitHub
  • Owner: jku-vds-lab
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 61.2 MB
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 3
  • Open Issues: 3
  • Releases: 7
Created almost 4 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.rst 

ParaDime: A Framework for Parametric Dimensionality Reduction
=============================================================

|ReadTheDocs Badge|  |License Badge|  |PyPi Badge|  |Black Badge|

ParaDime is a modular framework for specifying and training parametric dimensionality reduction (DR) models. These models allow you to add new data points to existing low-dimensional representations of high-dimensional data. ParaDime DR models are constructed from simple building blocks (such as the relations between data points), so that experimentation with novel DR techniques becomes easy.

Installation
============

ParaDime is available via PyPi through:

.. code-block:: text

    pip install paradime

ParaDime requires `Numpy `_, `SciPy `_, `scikit-learn `_, and `PyNNDescent `_  (see |req text|_ file), all of which are installed auomatically when installing ParaDime.

ParaDime also requires `PyTorch `_, which must be installed separately. If you want to train ParaDime routines on the GPU, make sure to install CUDA along with the correct ``cudatoolkit`` version. See the `PyTorch docs `_ for detailed installation info.

If you want to use ParaDime's plotting utilities, `Matplotlib `_ has to be installed additionally.

.. |req text| replace:: ``requirements.txt``
.. _req text: https://github.com/einbandi/paradime/blob/master/requirements.txt

Documentation
=============

For a simple example with one of the predefined ParaDime routines, see `Simple Usage `_ in the documentation.

More detailed information about how to set up cusom routines can be found in `Building Blocks of a ParaDime Routine `_.

For additional examples of varying complexity, see `Examples `_.

References
==========

.. [1] Van Der Maaten, L., Hinton, G. `“Visualizing data using t-SNE” `__, Journal of Machine Learning Research (2008).

.. [2] LeCun, Y., Cortes, C., Burges, C.J.C. `“The MNIST database of handwritten digits” `__ (1998).


.. |ReadTheDocs Badge| image:: https://readthedocs.org/projects/paradime/badge/?version=latest&style=flat-square
   :target: https://paradime.readthedocs.io/en/latest/index.html
   :alt: Documentation Status

.. |License Badge| image:: https://img.shields.io/github/license/einbandi/paradime?style=flat-square
   :target: https://mit-license.org/
   :alt: License

.. |PyPi Badge| image:: https://img.shields.io/pypi/v/paradime?style=flat-square
   :target: https://pypi.org/project/paradime/
   :alt: PyPi Version

.. |Black Badge| image:: https://img.shields.io/badge/code%20style-black-black?&style=flat-square
   :target: https://github.com/psf/black
   :alt: Code Style

Owner

  • Name: JKU Visual Data Science Lab
  • Login: jku-vds-lab
  • Kind: organization
  • Email: contact@jku-vds-lab.at
  • Location: Linz, Austria

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite our research article as below."
authors:
- family-names: "Hinterreiter"
  given-names: "Andreas"
  orcid: "https://orcid.org/0000-0003-4101-5180"
title: "ParaDime: A Framework for Parametric Dimensionality Reduction"
version: 1.1.0
date-released: 2022-08-30
url: "https://github.com/einbandi/paradime"
preferred-citation:
  type: article
  authors:
  - family-names: "Hinterreiter"
    given-names: "Andreas"
    orcid: "https://orcid.org/0000-0003-4101-5180"
  - family-names: "Humer"
    given-names: "Christina"
    orcid: "https://orcid.org/0000-0002-0249-4062"
  - family-names: "Kainz"
    given-names: "Bernhard"
    orcid: "https://orcid.org/0000-0002-7813-5023"
  - family-names: "Streit"
    given-names: "Marc"
    orcid: "https://orcid.org/0000-0001-9186-2092"
  doi: "10.48550/arXiv.2210.04582"
  journal: "arXiv: 2210.04582 [cs.LG]"
  month: 10
  start: 1 # First page number
  end: 10 # Last page number
  title: "ParaDime: A Framework for Parametric Dimensionality Reduction"
  # issue: 1
  # volume: 13
  year: 2022

GitHub Events

Total
Last Year

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 171
  • Total Committers: 1
  • Avg Commits per committer: 171.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
einbandi h****a@g****m 171

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 49
  • Total pull requests: 7
  • Average time to close issues: 6 days
  • Average time to close pull requests: less than a minute
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 0.49
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • 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
View more stats
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Dependencies

docs/requirements.txt pypi
  • numpy ==1.22.3
  • pynndescent ==0.5.7
  • scipy ==1.7.3
  • sphinx ==5.0.2
  • sphinx-autodoc-typehints ==1.18.3
  • sphinx-favicon ==0.2
  • sphinx-rtd-theme ==0.4.3
  • torch ==1.12.0
requirements.txt pypi
  • numpy >=1.21
  • pynndescent >=0.5
  • scikit-learn >=1.11
  • scipy >=1.7
  • torch >=1.11
pyproject.toml pypi
  • numpy >=1.21, <1.23
  • pynndescent >=0.5
  • scikit-learn >=1.1
  • scipy >=1.7