Hi-COVIDNet : Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea
Source code and datasets of the paper Hi-COVIDNet : Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea, KDD 2020 (AI for COVID track, Health Day)
Since the data from Korea Telecom(KT) is not open to the public, if you would like to run the code, please contact KT. We are contacting KT for data release.
Requirements
- Python 3.6 (Recommend Anaconda)
- Ubuntu 16.04.3 LTS
- Pytorch >= 1.2.0
- Numpy >= 1.17.2
- Download all codes (*.py) and put them in the same folder
- Create "model_grid_search" folder in the same folder
- Create "pickled_ds" folder for dataset and mean_std data
- Open terminal in the same folder
- Run "python data_loader.py" to preprocess and save data in ".pkl" format
python data_loader.py -h
usage: data_loader.py [-h] [--output_size O] [--save]
Hi-covidnet DATALOADER
optional arguments:
-h, --help show this help message and exit
--output_size O How many days you are predicting(default: 14)
--save Saving pre-processed dataexample usage :
python data_loader.py --output_size 14
data shape is 32 (14, 10)
target_continent shape is (32, 14, 6) target_total shape is (32, 14)
Loading KT roaming data
Loading infection ratio data
Loading passenger flights data
Normalizing continent target
Normalizing total target- Run "python main.py" to train Hi-COVIDNet
python main.py -h
usage: main.py [-h] [--epochs N] [--model_path MODEL_PATH] [--gpu_id GPU_ID]
[--lr LR] [--beta BETA] [--hidden_size HIDDEN]
[--output_size OUTPUT] [--is_aux] [--is_tm]
Hi-covidnet
optional arguments:
-h, --help show this help message and exit
--epochs N number of epochs to train (default: 100)
--model_path MODEL_PATH
prefix of path of the model
--gpu_id GPU_ID gpu_ids: e.g. 0,1,2,3,4,5
--lr LR learning rate (default: 0.03)
--beta BETA ratio of continent loss and total loss (default: 0.5)
--hidden_size HIDDEN hidden size of LSTM and Transformer(default: 4) e.g.
2,4,8, ... depending on your dataset
--output_size OUTPUT How many days you are predicting
--is_aux use auxilary data
--is_tm use transformerPlease check the hyperparameters of Hi-COVIDNet defined in main.py
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