ReDeLEx

ReDeLEx (Relational Deep Learning Exploration) is a Python framework for the development and evaluation of Relational Deep Learning (RDL) models. It enables end-to-end experimentation with graph-based neural networks on relational databases (RDBs), building on the CTU Relational Learning Repository and fully integrating with the RelBench interface.

It provides tools to transform SQL databases into heterogeneous graph representations suitable for Graph Neural Networks (GNNs), supports both static and temporal tasks, and enables a structured comparison across classical and deep learning models.

🧠 Features

• ✅ Supports direct SQL database connectivity (local or remote RDBMS)
• 🔗 Transforms relational schemas into heterogeneous graphs
• 🧩 Automates attribute type inference and encoding (categorical, numerical, text, time)
• 📦 Provides access to 70+ relational datasets from the CTU Repository
• 🧪 Supports benchmarking tasks including binary/multiclass classification, temporal tasks, and pretraining
• 🧠 Compatible with diverse neural architectures (e.g., GraphSAGE, Transformer-based models)
• 📊 Evaluates classical ML models (e.g., LightGBM, Propositionalization) alongside RDL models

📦 Installation

Install ReDeLEx via pip:

pip install redelex

If you're using RelBench, the CTU datasets are automatically supported.

🚀 Quickstart

Loading CTU datasets

Using RelBench interface:

from relbench.datasets import get_dataset
import redelex

dataset = get_dataset('ctu-seznam')
db = dataset.get_db()

Using ReDeLEx directly:

from redelex import datasets as ctu_datasets

dataset = ctu_datasets.Seznam()
db = dataset.get_db()

Loading a custom SQL database

from redelex.datasets import DBDataset

custom_dataset = DBDataset(
    dialect="mariadb",  # e.g. postgresql, sqlite, mysql
    driver="mysqlconnector",
    user="your_user",
    password="your_password",
    host="your_host",
    port=3306,
    database="your_database"
)

db = custom_dataset.get_db(upto_test_timestamp=False)

Note: For full examples of task and schema configuration, see examples in ctu_datasets.py.

📚 Tasks & Use Cases

ReDeLEx supports:

• Node-level prediction (static or temporal)
• Link prediction
• Pretraining tasks via value masking
• Database modification for complex task generation

Each task is backed by a training table and optionally a temporal schema.

🏗️ Architecture

RDL models in ReDeLEx are modular and consist of:

1. Attribute encoders for tabular data
2. Tabular models (optional, e.g. ResNet)
3. Graph Neural Network layers
4. Task-specific heads (e.g. MLP classifiers)

Supported model examples include:

• Linear SAGE
• Tabular ResNet + GraphSAGE
• DBFormer (Transformer-based)

📈 Experiments

ReDeLEx includes tools for:

• Selecting RDL-suitable datasets based on structure and size
• Comparing RDL with traditional ML and propositionalization
• Benchmarking across 70+ relational datasets from various domains

For experimental results and performance benchmarks, see the ECML PKDD 2025 paper (coming soon).

⚙️ Development

Install uv (recommended for managing environments)

• macOS & Linux

wget -qO- https://astral.sh/uv/install.sh | sh

• Windows

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

More info: https://docs.astral.sh/uv/getting-started/installation/

Install dependencies

CPU:

uv sync
uv pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.4.0+cpu.html

CUDA 12.4:

uv sync --no-group cpu --group cu124

Enable and run pre-commit

uv run pre-commit install
uv run pre-commit run

📜 Citation

If you use ReDeLEx in your work, please cite:

📎 Acknowledgements

This project has received funding from the European Union’s Horizon Europe program under the grant agreement TUPLES No. 101070149, and the Czech Science Foundation grant No. 24-11664S.