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Overview

Relational Deep Learning is a new approach for end-to-end representation learning on data spread across multiple tables, such as in a relational database (see our vision paper). RelBench is the accompanying benchmark which seeks to facilitate efficient, robust and reproducible research in this direction. It comprises of a collection of realistic, large-scale, and diverse datasets structured as relational tables, along with machine learning tasks defined on them. It provides full support for data downloading, task specification and standardized evaluation in an ML-framework-agnostic manner. Additionally, there is seamless integration with PyTorch Geometric to load the data as a graph and train GNN models, and with PyTorch Frame to encode the various types of table columns. Finally, there is a leaderboard for tracking progress.

RelBench is in its beta release stage, and we are planning to increase datasets and benchmarking in the near future. Datasets in the current version are subject to change.

Installation

You can install RelBench using pip:

pip install relbench

Package Usage

Here we describe key functions of RelBench. RelBench provides a collection of APIs for easy access to machine-learning-ready relational databases.

To see all available datasets:

from relbench.datasets import dataset_names
print(dataset_names)

For a concrete example, to obtain the rel-stackex relational database, do:

from relbench.datasets import get_dataset
dataset = get_dataset(name="rel-stackex")

To see the tasks available for this dataset:

print(dataset.task_names)

Next, to retrieve the rel-stackex-votes predictive task, which is to predict the upvotes of a post it will receive in the next 2 years, simply do:

task = dataset.get_task("rel-stackex-votes")
task.train_table, task.val_table, task.test_table # training/validation/testing tables

The training/validation/testing tables are automatically generated using pre-defined standardized temporal split. You can then build your favorite relational deep learning model on top of it. After training and validation, you can make prediction from your model on task.test_table. Suppose your prediction test_pred is an array following the order of task.test_table, you can call the following to retrieve the unified evaluation metrics:

task.evaluate(test_pred)

Additionally, you can evaluate validation (or training) predictions as such:

task.evaluate(val_pred, task.val_table)

Demos

List of working demos:

Cite RelBench

If you use RelBench in your work, please cite our paper:

@article{relbench,
  title={Relational Deep Learning: Graph Representation Learning on Relational Tables},
  author={Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec},
  year={2023}
}