Data Loading
- Connectors for Neo4j, Amazon Neptune, and relational databases
- Streams subgraphs — no need to load the full graph into memory
- Caching and prefetching hide database latency
- Reduces memory footprint by orders of magnitude
← Showcase
4.3 · Showcase
Open Source Contributions: Building the Commons
Three open source contributions — a production graph ML library, a hierarchical forecasting package, and rigorous reproducibility work — that advance the entire data science community by building shared foundations rather than solving identical problems in isolation.
Opening
These contributions demonstrate technical depth whilst advancing collective knowledge — spanning production tooling, hierarchical forecasting, and reproducible research. Each fills a genuine gap: the first bridges research and deployment, the second brings R's ecosystem to Python, and the third holds the field accountable to its own published claims.
Contribution 1
PyGraphML — Production-Ready Graph ML
Frameworks like PyTorch Geometric excel at research but lack what production deployments need: database connectors, MLOps integration, latency-optimised inference, and dynamic graph support. PyGraphML fills that gap.
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Apache
2.0 Licence
MLOps Integration
- MLflow for experiment tracking
- Weights & Biases for visualisation
- Model registries for versioning
- Prometheus for production monitoring
Optimised Inference
- Embedding caching for infrequently changing entities
- Batch inference to amortise fixed costs
- ONNX / TorchScript conversion for faster execution
- float16 / int8 quantisation — 5–10× faster than naïve implementations
Dynamic Graph Handling
Most research implementations assume static graphs — PyGraphML does not.
- Temporal snapshots let models access historical graph states
- Incremental updates enable efficient retraining without full reprocessing
- Versioning maintains multiple graph versions for A/B testing and rollback
Production adoption spans financial services and e-commerce; quarterly releases incorporate community bug fixes and features. Apache 2.0 permits both open source and commercial use.
Contribution 2
Time Series Reconciliation Package
R's fable ecosystem has long supported hierarchical forecasting; Python had no comparable tooling. This standalone package fills that gap with efficient reconciliation methods integrated with Prophet, statsmodels, and scikit-learn.
Reconciliation Methods
- Bottom-up — aggregate forecasts from the lowest level upwards
- Top-down — distribute high-level forecasts downwards by proportion
- Middle-out — start at intermediate levels; reconcile both up and down
- MinT (Optimal) — covariance-based; minimises forecast variance under coherence constraints
- Multiple covariance estimation options — trade accuracy against computational cost
Community Additions
- Cross-temporal reconciliation — coherence across different time granularities
- Probabilistic reconciliation — prediction intervals, not just point forecasts
- GPU acceleration — for very large hierarchies
API Design & Adoption
fit-reconcile pattern — generate base forecasts with any preferred method, then apply reconciliation in one step. Accepts Prophet, statsmodels SARIMAX, scikit-learn regressors, and pandas DataFrames. Handles datetime indexing, missing values, and irregular spacing; output format mirrors input.
Performance — sparse matrix operations throughout reduce memory from quadratic to linear in hierarchy size. Documentation includes conceptual guides, academic references, API docs, and worked examples (retail, energy, tourism) with Jupyter notebooks. Several thousand monthly downloads; used by retailers, energy companies, and forecasting consultancies.
Contribution 3
Reproducible Research Implementations
We implement papers with insufficient detail or incomplete official code — to deeply understand methods, provide working code for the community, and establish honestly whether published results hold up.
Where Reproduction Diverged
- Neural forecasting (N-BEATS, NHiTS): matched accuracy but training time far higher than claimed
- Interpretable ML (SHAP, LIME variants): undocumented parameter sensitivities rarely discussed in papers
- Papers omitting exact preprocessing steps, random seeds, and computational resources
Where Reproduction Succeeded
- Temporal graph networks outperformed published baselines after fixing subtle data-processing bugs
- Every decision and assumption documented; sensitivity to choices measured
- Scripts reproduce figures from papers where possible
Impact
- Implementations cited in subsequent research papers and incorporated into teaching materials
- Co-authored papers on reproducibility checklists; participated in reproducibility challenges
- Discussed in major ML communities, highlighting gaps between published claims and reproducible reality
Key Insight — Progress accelerates when we build on shared foundations—production-ready tools and reproducible implementations advance the entire community rather than repeatedly solving identical problems in isolation.
Key Takeaways
- Open source contributions advance the entire data science community
- Production-ready tools bridge gap between research and real-world deployment
- Reproducible implementations reveal discrepancies between claims and reality
- Transparent documentation and honest reporting build collective knowledge