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Bag of Factors allow you to analyze a corpus from its factors.
The feature_extraction module mimicks the module https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text
with a focus on character-based extraction.
The main differences are:
The main entry point for this module is the CountVectorizer class, which mimicks
its scikit-learn counterpart (also named CountVectorizer).
It is in fact very similar to sklearn's CountVectorizer using char or
char_wb analyzer option from that module.
The fuzz module mimicks the fuzzywuzzy-like packages like
The main difference is that the Levenshtein distance is replaced by the Joint Complexity distance. The API is also slightly change to enable new features:
fit) to accelerate the computation in
the case a stream of queries is sent against the same list of choices.The main fuzz entry point is the Process class.
Look at examples from the reference_ section.
This package was created with Cookiecutter_ and the francois-durand/package_helper_2_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _francois-durand/package_helper_2: https://github.com/francois-durand/package_helper_2
.. _reference: https://balouf.github.io/bof/reference/index.html
FAQs
Bag of Factors allow you to analyze a corpus from its self_factors.
We found that bof demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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