PyUoI contains implementations of Union of Intersections framework for a variety of penalized generalized linear models as well as dimensionality reductions 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:
- Separating the selection and estimation problems to simultaneously achieve sparse models with low-bias and low-variance parameter estimates.
- 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 the NeurIPS paper (Bouchard et al., 2017).
PyUoI is available for Python 3 on PyPI:
pip install pyuoi
and through conda-forge:
conda install pyuoi -c conda-forge
PyUoI requires
- numpy>=1.14
- h5py>=2.8
- scikit-learn>=0.20
and optionally
- pycasso
- mpi4py
to run.
To develop PyUoI you will additionally need
- cython
to build from source and
- pytest
- flake8
to run the tests and check formatting.
PyUoI is split up into two modules, with the following UoI algorithms:
linear_model(generalized linear models)- Lasso penalized linear regression UoILasso.
- Elastic-net penalized linear regression (UoIElasticNet).
- Logistic regression (Bernoulli and multinomial) (UoILogistic).
- Poisson regression (UoIPoisson).
decomposition(dimensionality reduction)- Column subset selection (UoICSS).
- Non-negative matrix factorization (UoINMF).
Similar to scikit-learn, each UoI algorithm has its own Python class.
Please see our ReadTheDocs page for an introduction to Union of Intersections, usage of PyUoI, and the API.
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