* Cython is too difficult to maintain and Numba dict management is relatively OK since last time. Time to switch!
0.3.5
* Attempt to update PyPi with Mac M1 compatible wheels.
0.3.4
* Renaming process.py to fuzz.py to emphasize that the module aims at being an alternative to the fuzzywuzzy package. * Removed modules FactorTree and JC. What they did is now essentially covered by the feature_extraction and fuzz modules. * General cleaning / rewriting of the documentation.
0.3.3
* 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
* 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
* 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).