bof 0.4.1

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

Bag of Factors

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

• Free software: MIT
• Documentation: https://balouf.github.io/bof/.

Features

Feature Extraction

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:

• it is slightly faster;
• the features can be incrementally updated;
• it is possible to fit only a random sample of factors to reduce space and computation time.

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.

Fuzz

The fuzz module mimicks the fuzzywuzzy-like packages like

• fuzzywuzzy (https://github.com/seatgeek/fuzzywuzzy)
• rapidfuzz (https://github.com/maxbachmann/rapidfuzz)

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:

• The list of possible choices can be pre-trained (fit) to accelerate the computation in the case a stream of queries is sent against the same list of choices.
• Instead of one single query, a list of queries can be used. Computations will be parallelized.

The main fuzz entry point is the Process class.

Getting Started

Look at examples from the reference_ section.

Credits

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