pyg-rnnconv 0.1.0

RNNConv module for PyTorch Geometric

pyg-rnn

A PyTorch Geometric RNN-based Convolution layer for temporal graph processing.

Installation

pip install pyg-rnn

Example usage

from pyg_rnn import RNNConv
import torch
from torch_geometric.data import Data

conv = RNNConv(torch.nn.GRU, edge_index=edge_index, event_time=event_time, in_channels=16, hidden_channels=32)

x = torch.randn((num_events, 16))
output = conv(x)
print(output.shape)

Why pyg-rnn

This Module allows creation of RNN layers within a PyG model without going through a padded tensor. Creating a padded tensor can be very prohibitive for a dataset with big lead and long tail For example, it you have 1 million groups of events, where most groups have 3-5 events (long take), but some have about 1000, you will need to create a padded tensor $10000001,000n$, where $n$ is the number of properties within each event. That can be 100 times more space than needed