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"

Installation

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

Usage

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

1. 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')
   

2. Generate a candidate dataset from the learnt features

   import pandas as pd import data.maker

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

Limitations

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, ...)
  

Credits :

• Ziqi Zhang
• Brad Malin
• Steve L. Nyemba