sgtsnepi 0.4

SG-t-SNE-Π wrapper for Python

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: Warning! This repository is still a work in progress and is not ready for general use.

Installation

Prerequisites

SG-t-SNE-Π uses the following open-source software:

• FFTW3 3.3.8
• METIS 5.1.0
• FLANN 1.9.1
• Intel TBB 2019
• LZ4 1.9.1
• Doxygen 1.8.14

Before installing the module you must install the software dependencies with your package manager of choice.

On Ubuntu:

sudo apt-get install libtbb-dev libflann-dev libmetis-dev libfftw3-dev liblz4-dev doxygen

On macOS:

brew install flann tbb metis fftw lz4 doxygen

Installing the Module

As of this point in the development it is recommended to work inside of a Python virtual environment

To install the package run

pip install git+https://github.com/alex-unofficial/sgtsnepi-python.git

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 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.

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}
}