SpaceAugmentation 0.0.1

augmentation

Augmentation_Package

This packages utilizes two augmentation methods namely. They were taken from this paper: https://arxiv.org/abs/1910.04176

1. Noise injection (aka radiation therapy): this simply upsamples a given list of embeddings with random noise.
2. Delta_S: Which takes in a given array and extrapolates data given a target label.

How To Install?

!pip install autograde-aug==0.0.1

How do i use them?

First you need to import the library and instantiate it

import Augmentation

ag = Augmentation.Augmentation()

Noise Injection:

l1, l2 = ag.add_noise(list_of_embeddings, list_of_labels)

l1 will be a new list with doubles the size including original embeddings + new embeddings l2 will be new list of labels

Chnage the level of noise you want to inject

l1, l2 = ag.add_noise(list_of_embeddings, list_of_labels, noise_low= 0.0, nose_high= 0.1)

Delta_S:

This stems from formula

X_hat =( Xi − Xj ) + Xk

Xi is random sample 1 Xj is random sample 2 Xk is random sample 3

Sample a pair of sentences (Xi, Xj) from the target category.

DELTAS applies deltas from the same target category to another sample Xk

 l1, l2 = ag.delta_S(list_of_embeddings, list_of_labels, target=0)

NEW !

This lambda with delta_s fusion is a novel technique that has not been tested yet or introduced yet.

if lambda_ is used then we use the lambda_ value times the delta

X_hat =( Xi − Xj ) * λ + Xk

 l1, l2 = ag.delta_S(list_of_embeddings, list_of_labels, target=0, lambda_= 0.3)

More Features will be added soon. Enjoy!