graphflex 0.1.1

Flexible Framework for Graph Feature Engineering

GraphFlex

Flexible Framework for Graph Feature Engineering

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

📦 Installation

pip install graphflex

Optional Dependencies

GraphFlex supports several optional extras. Install them with:

pip install "graphflex[dgl]"
pip install "graphflex[neo4j]"
pip install "graphflex[rdflib]"
pip install "graphflex[full]"  # all optional features

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🔍 Example Usage

# 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)

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✨ 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

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📚 Documentation

For full documentation, examples, and API reference, visit the GraphFlex repository.

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⚙ Dependencies

• Python ≥ 3.10
• numpy, pandas, scikit-learn, tqdm
• Optional: dgl, torch, torchdata, rdflib-hdt, neo4j, PyYAML, pydantic

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👤 Author

Bram Steenwinckel – [e-mail]

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📄 License

This project is licensed under the MIT License.