rusty1s

We are kicking off our third community sprint!

This community sprint resolves around improving test coverage across the PyG code base. Currently, our tests cover 85.68% of all code in PyG. The goal of the community sprint is to bump this number into the high 90s (and to get yourself more familiar with the various parts of the code base).

The sprint begins Friday Janurary 27th and will last 2 weeks. If you are interested in helping out, please also join our PyG slack channel #community-sprint-code-coverage for more information.

🚀 Improving Code Coverage

rusty1s

We are excited to announce the release of PyG 2.2 🎉🎉🎉

• Highlights
• Breaking Changes
• Deprecations
• Features
• Bugfixes
• Full Changelog

PyG 2.2 is the culmination of work from 78 contributors who have worked on features and bug-fixes for a total of over 320 commits since torch-geometric==2.1.0.

rusty1s

We are kicking off our very first community sprint!

The community sprint resolves around adding missing type hints and TorchScript support for various functions across PyG, aiming to improve and clean-up our core codebase. Each individual contribution is designed to only take around 30 minutes to two hours to complete.

The sprint begins Wednesday October 12th with a kick off meeting at 8am PST. The community sprint will last 2 weeks and we will have another live hangouts when the sprint has completed. If you are interested in helping out, please also join our PyG slack channel #community-sprint-type-hints for more information.

🚀 Add missing type hints and TorchScript support

rusty1s

The first pyg-lib releases focuses on unifying the implementations from torch-sparse and torch-cluster into a single package in order to reduce the number of external low-level library dependencies of PyG. In addition, implementations will be improved, e.g., by out-sourcing common routines into re-usable building blocks, unifiying the interfaces, supporting various data types, biased sampling, etc. New functionality will be integrated for temporal-based learning and GNN acceleration.

Samplers

• Unify common routines behind re-usable functions (e.g., sampling with/without replacement)
• Deterministic sampling routines
• Full support for different data types, e.g., int32, int64, etc
• Integration of cugraph dependency

rusty1s

We are excited to announce the release of PyG 2.1 🎉

• Highlights
• Breaking Changes
• Deprecations
• Features
• Bugfixes
• Full Changelog

PyG 2.1 is the culmination of work from over 60 contributors who have worked on features and bug-fixes for a total of over 320 commits since torch-geometric==2.0.4.

rusty1s

PyG 2.0.3 🎉

A new minor PyG version release, including a variety of new features and bugfixes:

Features

• GLNN: Graph-less Neural Networks [Example] (#3572)
• LINKX: Large Scale Learning on Non-Homophilous Graphs [Example] (#3654)
• Added an example for heterogeneous link classification (#3350) - thanks to @anniekmyatt
• HANConv: The Heterogenous Graph Attention operator [[Example](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/han_imd

rusty1s

A new minor version release, including further bugfixes, official PyTorch 1.10 support, as well as additional features and operators:

Features

• Added video tutorials and Colabs from the PyTorch Geometric Tutorial project (thanks to @AntonioLonga)
• Added the GraphMultisetTransformer operator (thanks to @JinheonBaek)
• Added the PointTransformerConv operator (thanks to @QuanticDisaster)
• Added the HEATConv operator (thanks to @Xiaoyu006)
• Added the [PNA](https://pytorch-geometric.readthedocs.io/en/latest/m

rusty1s

PyG 2.0 🎉 🎉 🎉

PyG (PyTorch Geometric) has been moved from my own personal account rusty1s to its own organization account pyg-team to emphasize the ongoing collaboration between TU Dortmund University, Stanford University and many great external contributors. With this, we are releasing PyG 2.0, a new major release that brings sophisticated heterogeneous graph support, GraphGym and many other exciting features to PyG.

Heterogeneous Graph Support

rusty1s

import os
import wget
import time
import errno
import argparse
import os.path as osp

import torch
from scipy.io import loadmat

rusty1s

(Visionary) Heterogeneous Graph Training API

We train on the Acquire Valued Shoppers Dataset in order to predict whether a customer will purchase a product again in case he/she has received a coupon for it in the past.

Offer (37):
-----------
id:               int   primary key
quantity          int
value             float