Title: GAIN: Missing Data Imputation using Generative Adversarial Nets
Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar
Reference: J. Yoon, J. Jordon, M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," International Conference on Machine Learning (ICML), 2018.
Paper Link: http://medianetlab.ee.ucla.edu/papers/ICML_GAIN.pdf
Appendix Link: http://medianetlab.ee.ucla.edu/papers/ICML_GAIN_Supp.pdf
Description of the code
This code shows the implementation of GAIN on MNIST dataset.
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Introducing 50% of missingness on MNIST dataset.
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Recover missing values on MNIST datasets using GAIN.
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Show the multiple imputation results on MNIST with GAIN.
Add source codes for UCI Letter and Spam datasets (02/12/2019)
pip3 install {package}
The code implementing MICE requires Python 3 (tested on 3.6.6) and scikit-learn (tested on 0.21.2).
Create directory for each dataset; e.g.,
mkdir news
Run with arguments dataname data file; e.g.,
python train_GAIN.py news News.csv
for UCI datasets, or run with arguments classification; e.g.,
python train_GAIN_MNST.py 0
for MNST dataset.
Run with arguments classification dataname; e.g.,
python3 train_MICE.py 0 news