PyUoI: The Union of Intersections Framework in Python

PyUoI contains implementations of Union of Intersections framework for a variety of penalized generalized linear models as well as dimensionality reduction techniques such as column subset selection and non-negative matrix factorization. In general, UoI is a statistical machine learning framework that leverages two concepts in model inference:

  1. Separating the selection and estimation problems to simultaneously achieve sparse models with low-bias and low-variance parameter estimates.

  2. Stability to perturbations in both selection and estimation.

PyUoI is designed to function similarly to scikit-learn, as it often builds upon scikit-learn’s implementations of the aforementioned algorithms.

Further details on the UoI framework can be found in [Bouchard2017] and [Ubaru2017].



Bouchard, K., Bujan, A., Roosta-Khorasani, F., Ubaru, S., Prabhat, M., Snijders, A., … & Bhattacharya, S. (2017). Union of intersections (UoI) for interpretable data driven discovery and prediction. In Advances in Neural Information Processing Systems (pp. 1078-1086).


Ubaru, S., Wu, K., & Bouchard, K. E. (2017, December). UoI-NMF cluster: a robust nonnegative matrix factorization algorithm for improved parts-based decomposition and reconstruction of noisy data. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 241-248). IEEE.

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