Skip to content

About

Python wrapper for SG-t-SNE-Π

Resources

Stars

0 stars

Watchers

2 watching

Forks

Latest commit

 

History

281 Commits

Folders and files

Repository files navigation

SG-t-SNE-Π wrapper for Python

This repository aims to provide a Python interface to SG-t-SNE-Π, which is a high-performance software for swift embedding of a large, sparse graph into a d-dimensional space (d=1,2,3) on a shared-memory computer.

⚠️ Warning! This package is still a work in progress.

Installation

Prerequisites

sgtsnepi supports Linux and macOS. Windows support is coming soon.

Installation requires Python 3.9 or higher.

Wheels are not built for all architectures as of now. If your architecture is not supported please open a relevant issue on the GitHub page.

Installing the Module

To install the package run

pip install sgtsnepi

Then in your Python code run the following to import the library:

import sgtsnepi

# [ ... ]

sgtsnepi.sgtsnepi(sparse_matrix, args)

Or to import the sgtsnepi function directly:

from sgtsnepi import sgtsnepi

# [ ... ]

sgtsnepi(sparse_matrix, args)

Running the Demo

To use the demo you should install matplotlib by running

pip install scipy matplotlib

Then from the source directory run

python tests/demo.py mm_file.mtx ndim

where mm_file.mtx is a file containing the input matrix which is in the Matrix Market file format, and ndim is the number of embedding dimensions.

You can find some compressed MatrixMarket files in the data/ subdirectory.

Citation

If you use this software, pleace cite the following paper:

@inproceedings{pitsianis2019sgtsnepi,
    author = {Pitsianis, Nikos and Iliopoulos, Alexandros-Stavros and Floros, Dimitris and Sun,        Xiaobai},
    doi = {10.1109/HPEC.2019.8916505},
    booktitle = {IEEE High Performance Extreme Computing Conference},
    month = {11},
    title = {{Spaceland Embedding of Sparse Stochastic Graphs}},
    year = {2019}
}

About

Python wrapper for SG-t-SNE-Π

Resources

Stars

0 stars

Watchers

2 watching

Forks

Used by

Contributors

Languages