Federated Conditional Generative Adversarial Nets Imputation method for Air Quality Missing Data

This project is the algorithm codes of paper "Federated Conditional Generative Adversarial Nets Imputation method for Air Quality Missing Data".

Requires

Pytorch >= 1.5

python >= 3.6

apex

matplotlib

json

We have uploaded the experiment data sets in the file 'Air_quality_dataset'.

Fisrt step:
- Run the 'CreateAndLoadDatasets' script to create the training dataset and test dataset.
Second step:
- You just need to run the 'Fed_gain_mian.py'. If you want to modify or change some parameters, you can set them in 'param_options.py' or when running code file in cmd adding '--' parameter.

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
air_quality_datasets		air_quality_datasets
CreateAndLoadDatasets.py		CreateAndLoadDatasets.py
EM_algorithm.py		EM_algorithm.py
FedAvg.py		FedAvg.py
Fed_gain_main.py		Fed_gain_main.py
Fed_gain_test_run.py		Fed_gain_test_run.py
GAIN_model.py		GAIN_model.py
GAIN_pretty_tqdm.py		GAIN_pretty_tqdm.py
GAIN_v2.py		GAIN_v2.py
GAN_AQDI.py		GAN_AQDI.py
GAN_AQDI_test_run.py		GAN_AQDI_test_run.py
GAN_training.py		GAN_training.py
LoadandCreateDatasets.py		LoadandCreateDatasets.py
Other_algos.py		Other_algos.py
README.md		README.md
Update.py		Update.py
WAGIN_model.py		WAGIN_model.py
WGAN_AQDI.py		WGAN_AQDI.py
__init__.py		__init__.py
data_explore.py		data_explore.py
function_test.py		function_test.py
param_options.py		param_options.py
plot_ex_results.py		plot_ex_results.py
plot_indexes_resluts.py		plot_indexes_resluts.py
show_dataset.py		show_dataset.py
util_tools.py		util_tools.py