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:

Train the GAN on the original/raw dataset

import pandas as pd
import data.maker

df      = pd.read_csv('sample.csv')
column  = 'gender'
id      = 'id' 
context = 'demo'
data.maker.train(context=context,data=df,column=column,id=id,logs='logs')

The trainer will store the data on disk (for now) in a structured folder that will hold training models that will be used to generate the synthetic data.

Generate a candidate dataset from the learned features

import pandas as pd
import data.maker

df  = pd.read_csv('sample.csv')
id  = 'id'
column = 'gender'
context = 'demo'
data.maker.generate(context=context,data=df,id=id,column=column,logs='logs')

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, ...)
  

• For now will only perform on a single feature.

Credits :

• Chao Yan
• Ziqi Zhang
• Brad Malin
• Steve L. Nyemba