gnn-ml-integration

What problem does it solve?

Integrates machine learning with GNN pipelines by extracting features from GNN models, orchestrating ML model training, and verifying framework availability to streamline end-to-end experiments.

Core Features & Use Cases

• Feature extraction: derives real GNN features from model markdown files to feed ML processes.
• Model training & evaluation: trains and evaluates ML models across multiple frameworks (scikit-learn, PyTorch, TensorFlow, JAX) and reports performance.
• Framework availability checks: detects installed ML frameworks and versions to guide pipeline execution.
• Use Case: In Active Inference workflows, ML integration wires GNN outputs into training, evaluation, and deployment tasks.

Quick Start

Run the ML integration step to process GNN files and generate ML-ready outputs.