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A Python library for replacing the missing variation in your data.

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Why should I use this?

Data collected for model training necessarily undersamples the likely variance in the input space. This library is a collection of tools for inserting typical kinds of perturbations to better approximate population variance; and, for creating similar-but-incorrect examples to aid in reducing the total size of the hypothesis space. These are commonly known as <small>ENRICHMENT</small> and <small>NEGATIVE SAMPLING</small>, respectively.

How do I use this?

Functions in niacin are separated into submodules for specific data types. Functions expose a similar API, with two input arguments: the data to be transformed, and the probability of applying a specific transformation.


data = "This is the song that never ends and it goes on and on my friends"
print(text.add_misspelling(data, p=1.0))
This is teh song tath never ends adn it goes on anbd on my firends

negative sampling:

data = "This is the song that never ends and it goes on and on my friends"
print(text.add_hypernyms(data, p=1.0))
This is the musical composition that never extremity and it exit on and on my person

How do I install this?

with pip:

pip install niacin

from source:

git clone && cd niacin && python install

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Files for niacin, version 0.2.0
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Filename, size niacin-0.2.0-py3-none-any.whl (2.0 MB) File type Wheel Python version py3 Upload date Hashes View

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