https://github.com/predict-idlab/graphflex

Flexible Framework for Graph Feature Engineering
https://github.com/predict-idlab/graphflex

Last synced: 3 months ago
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Flexible Framework for Graph Feature Engineering

• Host: GitHub
• URL: https://github.com/predict-idlab/graphflex
• Owner: predict-idlab
• License: mit
• Created: 2025-03-06T14:19:28.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-12T15:11:38.000Z (about 1 year ago)
• Last Synced: 2025-09-25T00:34:56.874Z (8 months ago)
• Language: Python
• Homepage:
• Size: 19 MB
• Stars: 0
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# GraphFlex

**Flexible Framework for Graph Feature Engineering**

[![PyPI](https://img.shields.io/pypi/v/graphflex?color=blue&label=PyPI&logo=pypi)](https://pypi.org/project/graphflex/)

[![Python Version](https://img.shields.io/pypi/pyversions/graphflex?logo=python)](https://pypi.org/project/graphflex/)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/predict-idlab/graphflex/python-publish.yml)

[![Docs](https://img.shields.io/badge/docs-online-blue?logo=readthedocs)](https://github.com/predict-idlab/graphflex)

[![Scikit-learn compatible](https://img.shields.io/badge/Scikit--learn-compatible-blue)](https://scikit-learn.org/stable/related_projects.html#third-party-projects)

---

GraphFlex is a modular and extensible framework for graph-based feature engineering in Python. It allows seamless integration of graph datasets with traditional machine learning pipelines using familiar tools like `scikit-learn`.

> 🔗 Homepage & Documentation: [GraphFlex on GitHub](https://github.com/predict-idlab/graphflex)

## 📦 Installation

```bash

pip install graphflex

```

### Optional Dependencies

GraphFlex supports several optional extras. Install them with:

```bash

pip install "graphflex[dgl]"

pip install "graphflex[neo4j]"

pip install "graphflex[rdflib]"

pip install "graphflex[full]"  # all optional features

```

---

## 🔍 Example Usage

```python

# GraphFlex pipeline

from graphflex import GraphFlex

from graphflex.functions.postprocessing.filter import NonUniqueFeatureFilter

from graphflex.functions.feature import MeanStdFeature

from graphflex.functions.edgenode import NumericalEdgeNode

connect = Connector(...) #use defined connector here

gflex = GraphFlex(

    connector=connect,

    node_feature=MeanStdFeature(),

    edge_node_feature=NumericalEdgeNode(),

    post_processor=NonUniqueFeatureFilter()

)

nodes = ...

feature_matrix = gflex.fit_transform(nodes)

```

---

## ✨ Features

- Plug-and-play feature extraction for graph nodes

- Compatible with `scikit-learn` pipelines

- Support for multiple graph backends (DGL, RDFLib-HDT, Neo4j, ...)

- Built-in feature functions and postprocessing modules

- Easily extendable with custom logic

---

## 📚 Documentation

For full documentation, examples, and API reference, visit the [GraphFlex repository](https://github.com/predict-idlab/graphflex).

---

## ⚙ Dependencies

- Python ≥ 3.10

- `numpy`, `pandas`, `scikit-learn`, `tqdm`

- Optional: `dgl`, `torch`, `torchdata`, `rdflib-hdt`, `neo4j`, `PyYAML`, `pydantic`

---

## 👤 Author

**Bram Steenwinckel** – [bram.steenwinckel@ugent.be](mailto:bram.steenwinckel@ugent.be)

---

## 📄 License

This project is licensed under the MIT License.