ddmo Documentation

Data-Driven Models for Optimization
Cheap surrogates for expensive functions

ddmo fits least-squares, radial basis function, kriging and ensemble surrogates to sampled data, with analytic gradients, uncertainty estimates and a dashboard for comparing models.

Get started → Theory & algorithms API reference
LSRBFKrigingWeightedEnsembleCollinearityFilter
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User guide Install the package, fit your first surrogate and pick the right model family.
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Theory & algorithms The mathematics behind each surrogate, with typeset pseudocode for the training algorithms.
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Backend Data ingestion, train/test splitting, ranking strategies and model export contracts.
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Tutorials Compare models, screen features, export trained models and drive optimizers with gradients.
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Benchmarks 4080 fits on seven engineering problems: pass rates, learning curves, cost and robustness.

Highlights

  • Unified fit/predict/score interface for all surrogate models.

  • Analytic gradients for optimization workflows.

  • Mathematical definitions for least-squares, radial basis functions, kriging, and weighted ensembles.

  • Backend service documentation for data ingestion, train/test splitting, ranking, and model export.

  • Step-by-step tutorials and typeset, cross-referenceable algorithm listings.

Scope

The core package lives in src/ddmo, the dashboard backend logic in src/ddmo_backend, and the Dash frontend in src/ddmo_frontend. The backend pages document the numerical contracts, ranking logic, and persistence conventions that govern those layers.

Build locally

Install the documentation dependencies and build the HTML site:

pip install -e ".[docs,ui,test]"
sphinx-build -b html docs docs/_build/html

Open docs/_build/html/index.html in a browser after a successful build.