When one uses hashing trick from sci-kit-learn, one ends up with a sparse matrix.
How can then one work with such a sparse matrix to still implement field-aware factorization machines? SKLearn does not have an implementation of FFM.
EDIT 1: I want to perform feature-hashing/hashing-trick for sure in order to be able to scale FFM to millions of features.
EDIT 2: Pandas is not able to scale to many fields. I also want to convert an arbitrary CSV (containing numerical and categorical features) into LIBFFM (field:index:value) format and perform hashing trick at the same time (preferably without using Pandas). Pandas2FFM does not scale even after performing the Hashing Trick.