Bof

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* 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.


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* 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.


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* 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.


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* 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).


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* The core is now the CountVectorizer class. Lighter and faster. Only features are kept inside.
* New process module inspired by fuzzywuzzy!


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* Full refactoring to make the package fit/transform compliant.
* Add a fit_sampling method that allows to fit only a (random) subset of factors


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