data-maker

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Introduction

This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques

- Generative Adversarial Networks
- With "Earth mover's distance"

pip install git+https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git@release

After installing the easiest way to get started is as follows (using pandas). The process is as follows:

Train the GAN on the original/raw dataset

 import pandas as pd
 import data.maker

 df  = pd.read_csv('myfile.csv')
 cols= ['f1','f2','f2']  
 data.maker.train(data=df,cols=cols,logs='logs')

Generate a candidate dataset from the learnt features

import pandas as pd import data.maker

df = data.maker.generate(logs='logs') df.head()

GANS will generate data assuming the original data has all the value space needed:

No new data will be created

  Assuming we have a dataset with an gender attribute with values [M,F]. 
  The synthetic data will not be able to generate genders outside [M,F]

Not advised on continuous values

  GANS work well on discrete values and thus are not advised to be used.
  e.g:measurements (height, blood pressure, ...)