bof 0.3.3

Bag of Factors allow you to analyze a corpus from its self_factors.

Bag of Factors

Bag of Factors allow you to analyze a corpus from its factors.

• Free software: GNU General Public License v3

• Documentation: https://balouf.github.io/bof/.

Features

• TODO

Credits

This package was created with Cookiecutter and the francois-durand/package_helper_2 project template.

History

0.3.3 (2021-01-01): Cython/Numba balanced

• All core CountVectorizer methods ported to Cython. Roughly 2.5X faster than sklearn counterpart (mainly because some features like min_df/max_df are not implemented).

• Process numba methods NOT converted to Cython as Numba seems to be 20% faster for csr manipulation.

• Numba functions are cached to avoid compilation lag.

0.3.2 (2020-12-30): Going Cython

• First attempt to use Cython

• Right now only the fit_transform method of CountVectorizer has been cythonized, for testing wheels.

• If all goes well, numba will probably be abandoned and all the heavy-lifting will be in Cython.

0.3.1 (2020-12-28): Simplification of core algorithm

• Attributes of the CountVectorizer have been reduced to the minimum: one dict!

• Now faster than sklearn counterpart! (The reason been only one case is considered here so we can ditch a lot of checks and attributes).

0.3.0 (2020-12-15): CountVectorizer and Process

• The core is now the CountVectorizer class. Lighter and faster. Only features are kept inside.

• New process module inspired by fuzzywuzzy!

0.2.0 (2020-12-15): Fit/Transform

• Full refactoring to make the package fit/transform compliant.

• Add a fit_sampling method that allows to fit only a (random) subset of factors

0.1.1 (2020-12-12): Upgrades

• Docstrings added

• Common module (feat. save/load capabilities)

• Joint Complexity module

0.1.0 (2020-12-12): First release

• First release on PyPI.

• Core FactorTree class added.