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52 lines
1.4 KiB
Markdown
52 lines
1.4 KiB
Markdown
## Introduction
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This package is designed to generate synthetic data from a dataset from an original dataset using deep learning techniques
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- Generative Adversarial Networks
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- With "Earth mover's distance"
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## Installation
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pip install git+https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git@release
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## Usage
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After installing the easiest way to get started is as follows (using pandas). The process is as follows:
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1. Train the GAN on the original/raw dataset
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import pandas as pd
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import data.maker
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df = pd.read_csv('myfile.csv')
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cols= ['f1','f2','f2']
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data.maker.train(data=df,cols=cols,logs='logs')
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2. Generate a candidate dataset from the learnt features
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import pandas as pd
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import data.maker
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df = data.maker.generate(logs='logs')
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df.head()
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## Limitations
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GANS will generate data assuming the original data has all the value space needed:
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- No new data will be created
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Assuming we have a dataset with an gender attribute with values [M,F].
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The synthetic data will not be able to generate genders outside [M,F]
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- Not advised on continuous values
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GANS work well on discrete values and thus are not advised to be used.
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e.g:measurements (height, blood pressure, ...)
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## Credits :
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- [Ziqi Zhang](ziqi.zhang@vanderbilt.edu)
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- [Brad Malin](b.malin@vanderbilt.edu)
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- [Steve L. Nyemba](steve.l.nyemba@vanderbilt.edu) |