scikit-matter#
scikit-matter is a toolbox of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows.
Features and Samples Selection
Supervised and unsupervised selection methods based on CUR matrix decomposition and Farthest Point Sampling.
Principal Covariates Regression
Utilises a combination between a PCA-like and a LR-like loss to determine the decomposition matrix to project feature into latent space
Having problems or ideas?#
Having a problem with scikit-matter? Please let us know by submitting an issue.
Submit new features or bug fixes through a pull request.
If you would like to contribute to scikit-matter, check out our Contributing page!