COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies (AAAI-22)
- Source code and datasets of the paper COVID-EENet: Predicting Fine-Grained Impact of COVID-19 on Local Economies.
- Since dataset from BCCard is not open to public, we only provide epidemic-view feature and the physical distance dataset of geography-view feature.
Requirements
- Ubuntu 16.04.7 LTS
- python 3.8 (Recommend Anaconda)
- Pytorch >= 1.9.0
- Run
python main.pyto train COVID-EENet
python main.py -h
usage: main.py [-h] [--model_name MODEL_NAME] [--fname FNAME] [--pred_len PRED_LEN] [--cuda CUDA] [--train] [--test] [--save_prediction] [--save_metric_result]
COVIDEENet
optional arguments:
-h, --help show this help message and exit
--model_name MODEL_NAME type one of the comparing algorithms including COVID-EENet
--fname FNAME type the file name of the parameters, predictions, experiment results
--pred_len PRED_LEN type the predicting length of algorithms
--cuda CUDA type the number of gpu
--train type when you train the model
--test type when you validate the model
--save_prediction type when you save the predictions of the algorithms
--save_metric_result type when you save the experiment results of the algorithms- the learned parameters in directory
models_state_dict - the predictions in directory
model_prediction - the experiment results in directory
RMSE_district_buz_pairs
Please check the hyperparameters of COVID-EENet defined in Config.py and supplementary material.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent