Bag of Factors#

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

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

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 package_helper_2 project template.