System requirements: Python 3.7.1 Tensorflow 2.2.0 PyTorch 1.2.0 GPU - NVIDIA GeForce RTX 2080

JupyterNotebook: 6.0.3

===============================Data=======================================

All data initial files are in data folder. AirQuality- data->air->initial MIMIC- data->mimic->initial

===============================Data Preprocessing=========================

Run data_preporcessAir.ipynb for preprocessing AirQuality data

Run data_preporcessMimic.ipynb for preprocessing MIMIC data

All data preprocessed files are in "data" folder.

AirQuality-

data->air->preprocess

MIMIC-

data->mimic->preprocess

===============================Bi-GAN==========================

To run Bi-GAN for EHR dataset - Run "biGan/main_ganOrig.ipynb" For training model- Set train=True

For Imputation Testing model-
	Set evalImp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set evalPred=True
	Set pred_len=8 or 7 or 6 or 5

===============================BRITS-I============================

BRITS-I To run BRITS-I for EHR dataset - Run "britsI/main - original.ipynb" For training model- Set train=True

For Imputation Testing model-
	Set evalImp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set evalPred=True
	Set pred_len=8 or 7 or 6 or 5

===============================Baseline=========================

Baseline MRNN - Run "mrnn/mrnnBaseline.ipynb" Input Arguments to be set -

For Imputation Testing model-
	Set imp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set pred=True
	Set pred_len=8 or 7 or 6 or 5

MICE - Run "baseline/MICE.ipynb" Input Arguments to be set -

For Imputation Testing model-
	Set imp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set pred=True
	Set pred_len=8 or 7 or 6 or 5

KNN- Run "baseline/knn.ipynb" Input Arguments to be set -

For Imputation Testing model-
	Set imp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set pred=True
	Set pred_len=8 or 7 or 6 or 5

MEAN- Run "baseline/mean.ipynb" Input Arguments to be set -

For Imputation Testing model-
	Set imp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set pred=True
	Set pred_len=8 or 7 or 6 or 5

===============================Bi-GAN Components=================

Bi-GAN without Discriminator=====================================

To run biWgan for EHR dataset - Run "biWgan/main_Wgan.ipynb.ipynb" For training model- Set train=True

For Imputation Testing model-
	Set evalImp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set evalPred=True
	Set pred_len=8 or 7 or 6 or 5

Bi-GAN without Lambda=======================================

To run lambda for EHR dataset - Run "lambda/main_ganLambda.ipynb" For training model- Set train=True

For Imputation Testing model-
	Set evalImp=True
	Set missingRate=10 or 20 or 30 or 40 or 50

For Prediction Testing model-
	Set evalPred=True
	Set pred_len=8 or 7 or 6 or 5

NOTE: Change path of files as required