Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar
Paper: Jinsung Yoon, James Jordon, Mihaela van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," International Conference on Machine Learning (ICML), 2018.
Paper Link: http://proceedings.mlr.press/v80/yoon18a/yoon18a.pdf
Contact: [email protected]
This directory contains implementations of GAIN framework for imputation using two UCI datasets.
- UCI Letter (https://archive.ics.uci.edu/ml/datasets/Letter+Recognition)
- UCI Spam (https://archive.ics.uci.edu/ml/datasets/Spambase)
To run the pipeline for training and evaluation on GAIN framwork, simply run python3 -m main_letter_spam.py.
Note that any model architecture can be used as the generator and discriminator model such as multi-layer perceptrons or CNNs.
- data_name: letter or spam
- miss_rate: probability of missing components
- batch_size: batch size
- hint_rate: hint rate
- alpha: hyperparameter
- iterations: iterations
$ python3 main_letter_spam.py --data_name spam
--miss_rate: 0.2 --batch_size 128 --hint_rate 0.9 --alpha 100
--iterations 10000- imputed_data_x: imputed data
- rmse: Root Mean Squared Error