pocket_dimension 0.1.3

Memory-efficient, dense, random projection of sparse vectors

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Pocket Dimension provides a memory-efficient, dense, random projection of sparse vectors. This random projection is the used to be able to take records {“id”: str, “features”: List[bytes], “counts”: List[int]}, convert them into sparse random vectors using scikit-learn’s FeatureHasher, and then project them down to lower dimensional dense vectors.

When the very large sparse universe becomes too inhospitable, escape into a cozy pocket dimension.

Documentation

Documentation for the API and theoretical foundations of the algorithms can be found at https://mhendrey.github.io/pocket_dimension

Installation

Pocket Dimension may be install using pip:

pip install pocket_dimension

I’m working on a conda-forge version, but this uses pybloomfiltermmap3 which is currently only on PyPi.