Skip to main content

HeXtractor is a tool designed to automatically convert selected data in tabular format into a PyTorch Geometric heterogeneous graph. As research into graph neural networks (GNNs) expands, the importance of heterogeneous graphs grows. However, data often comes in tabular form, and manually transforming this data into graph format can be tedious and error-prone. HeXtractor aims to streamline this process, providing researchers and practitioners with a more efficient workflow.

Project description

Logo

Overview

HeXtractor is a tool designed to automatically convert selected data in tabular format into a PyTorch Geometric heterogeneous graph. As research into graph neural networks (GNNs) expands, the importance of heterogeneous graphs grows. However, data often comes in tabular form, and manually transforming this data into graph format can be tedious and error-prone. HeXtractor aims to streamline this process, providing researchers and practitioners with a more efficient workflow.

Features

  1. Automatic Conversion: Converts tabular data into heterogeneous graphs suitable for GNNs.
  2. Support for Multiple Formats: Handles various tabular data formats with ease.
  3. Integration with PyTorch Geometric: Directly creates graphs that can be used with PyTorch Geometric.
  4. isualization: Utilizes NetworkX and PyVis for graph visualization.

Why HeXtractor?

Heterogeneous graphs are crucial in many applications of graph neural networks, yet creating them from tabular data manually is often cumbersome. HeXtractor automates this process, allowing researchers to focus on developing and training their models instead of data preprocessing.

Technologies

  1. Python: The primary programming language used for HeXtractor.
  2. pandas: Utilized for data manipulation and handling tabular data.
  3. PyTorch Geometric: Framework for creating and working with graph neural networks.
  4. NetworkX: Used for creating and managing complex graph structures.
  5. PyVis: Enables interactive visualization of graphs.

Installation

From PyPI

To install the latest version from PyPI run:

pip install hextractor

Manual from source code

  1. Make sure, that you have Anaconda or Miniconda installed. 2.Then, create new conda env from the provided environment.yml file:
conda env create -f environment.yml
  1. Activate environment:
conda activate hextractor
  1. Install poetry - main package manager used by this project
pip install poetry
  1. Install the package with all dependencies:
poetry install --with dev --with research

To use package, remember to activate the environment.

Documentation

You can find an official, detailed documentation here.

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

hextractor-1.0.1.tar.gz (17.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hextractor-1.0.1-py3-none-any.whl (21.5 kB view details)

Uploaded Python 3

File details

Details for the file hextractor-1.0.1.tar.gz.

File metadata

  • Download URL: hextractor-1.0.1.tar.gz
  • Upload date:
  • Size: 17.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.11.10 Linux/6.8.0-54-generic

File hashes

Hashes for hextractor-1.0.1.tar.gz
Algorithm Hash digest
SHA256 d3b997b451be389aae9f684cef5518a4023d04181f2a07c60138553c19296e1a
MD5 40f2ed4f7e29a72e6d7688f0b4dcfd68
BLAKE2b-256 63ad5932ed9d5deb8f101bf36ab53a93ddf0067be723d2a58b532f600eb58151

See more details on using hashes here.

File details

Details for the file hextractor-1.0.1-py3-none-any.whl.

File metadata

  • Download URL: hextractor-1.0.1-py3-none-any.whl
  • Upload date:
  • Size: 21.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.11.10 Linux/6.8.0-54-generic

File hashes

Hashes for hextractor-1.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 68515ddfa673f4026b1955e95acc7ba38e8b14ff6eecb54818a5e91be08b3673
MD5 060e99f22717a1996a18304bed0805f2
BLAKE2b-256 f1fc92a84ace8372ab957c813f06f0b6d34214229e7518e6060883380a619c7d

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