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
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
- Automatic Conversion: Converts tabular data into heterogeneous graphs suitable for GNNs.
- Support for Multiple Formats: Handles various tabular data formats with ease.
- Integration with PyTorch Geometric: Directly creates graphs that can be used with PyTorch Geometric.
- 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
Python: The primary programming language used for HeXtractor.pandas: Utilized for data manipulation and handling tabular data.PyTorchGeometric: Framework for creating and working with graph neural networks.NetworkX: Used for creating and managing complex graph structures.PyVis: Enables interactive visualization of graphs.
Installation
From PyPI
To install the latest version from PyPI run:
pip install hextractor
Manual from source code
- 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
- Activate environment:
conda activate hextractor
- Install poetry - main package manager used by this project
pip install poetry
- 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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
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
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d3b997b451be389aae9f684cef5518a4023d04181f2a07c60138553c19296e1a
|
|
| MD5 |
40f2ed4f7e29a72e6d7688f0b4dcfd68
|
|
| BLAKE2b-256 |
63ad5932ed9d5deb8f101bf36ab53a93ddf0067be723d2a58b532f600eb58151
|
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
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
68515ddfa673f4026b1955e95acc7ba38e8b14ff6eecb54818a5e91be08b3673
|
|
| MD5 |
060e99f22717a1996a18304bed0805f2
|
|
| BLAKE2b-256 |
f1fc92a84ace8372ab957c813f06f0b6d34214229e7518e6060883380a619c7d
|