This is a preliminary work to produce a scikit-learn transformer that transforms an input matrix of shape (n_samples, n_features) into a binary matrix of size (n_samples, n_new_features). Continous features are modified and extended into binary features, using linearly or inter-quantiles spaced bins. Discrete features are binary encoded with K columns, where K is the number of modalities. Other features (none of the above) are left unchanged.
This work have been updated and integrated to the tick module as a preprocessing tool (here) and used in the paper "Binarsity: a penalization for one-hot encoded features" available here and accepted for publication in JMLR.