SkData: Data Sets and Algorithm Evaluation Protocols in Python
James Bergstra, Nicolas Pinto, David Cox · Proceedings of the Python in Science Conferences · 2013
Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y.Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g.pandas or sklearn.The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented.The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research.This paper describes the usage and architecture of the SkData library.