The official PyTorch implementation of "Learning to Simulate Daily Activities via Modeling Dynamic Human Needs" (WWW'23).
The code is tested under a Linux desktop with torch 1.7 and Python 3.7.10.
- Tested OS: Linux
- Python >= 3.7
- PyTorch == 1.7.1
- Install PyTorch 1.7.1 with the correct CUDA version.
- Use the
pip install -r requirements. txtcommand to install all of the Python modules and packages used in this project.
Use the following command to train SAND on the Foursquare dataset:
cd SAND;
python app.py --dataset 'Foursquare' --mode 'train'
or on the Mobile Operator dataset:
python app.py --dataset 'Mobile' --mode 'train'
or on the Synthetic Operator dataset:
python app.py --dataset 'Synthetic' --mode 'train'
The trained models are saved in model/TIME/.
Use the following command to generate activity data on the Foursquare dataset:
cd SAND;
python app.py --dataset 'Foursquare' --mode 'generate' --generate_final_path your_path
Please specify your own path by the command-line argument generate_final_path for saving the generated data. Then the generated activity data will be in your_path/gen_data.json.
The implemention is based on NJSDE.
If you found this library useful in your research, please consider citing:
@inproceedings{yuan2023learning,
title={Learning to Simulate Daily Activities via Modeling Dynamic Human Needs},
author={Yuan, Yuan and Wang, Huandong and Ding, Jingtao and Jin, Depeng and Li, Yong},
booktitle={Proceedings of the ACM Web Conference 2023},
pages={906--916},
year={2023}
}