bug fix, and documentation

This commit is contained in:
Steve Nyemba 2019-12-12 12:13:31 -06:00
parent 99bc98aba5
commit 8a5307c242
2 changed files with 50 additions and 3 deletions

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@ -2,7 +2,7 @@ from ubuntu
RUN ["apt-get","update"]
RUN ["apt-get","upgrade","-y"]
RUN ["apt-get","install","-y","git", "python3-dev","tmux","locales","python3-pip","python3-numpy","python3-pandas","locales"]
RUN ["pip3","install","pandas-gbq","tensorflow"]
RUN ["pip3","install","pandas-gbq","tensorflow","git+https://hiplab.mc.vanderbilt.edu/git/aou/"]
RUN ["mkdir","-p","/usr/apps"]
WORKDIR /usr/apps
RUN ["git","clone","https://hiplab.mc.vanderbilt.edu/git/aou/gan.git@release","aou-gan"]
RUN ["git","clone","https://hiplab.mc.vanderbilt.edu/git/aou/bridge.git@release","aou-gan"]

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@ -1,2 +1,49 @@
# bridge
## 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 to synthesize things like measurements (height, blood pressure, ...)