
# SNIP
This is a pytorch implementation of SNIP, a submission for ICLR 2026.
This is the under review version.

## Requirements
Python 3.9.12, torch 1.13.1+cu117, numpy 1.26.4, einops 0.8.0, scipy 1.13.1, scikit-learn 1.6.1

## Data Source:
* PEMS04: [https://github.com/guoshnBJTU/ASTGNN](https://github.com/guoshnBJTU/ASTGNN)
* EPeMS:  [https://github.com/mb-Ma/STEV](https://github.com/mb-Ma/STEV)
* SeaLoop: [https://github.com/Sam1224/KITS](https://github.com/Sam1224/KITS)
* nrel_al: [https://github.com/Sam1224/KITS](https://github.com/Sam1224/KITS)
For your convinence, we has already provided these raw data in `raw_data` folder. 

## Note
Before training, please replace the `data_root_path` in `utils/dataset_provider.py` and `utils/graph_utils.py` with the absolute path of `raw_data` folder.

## Reproduction
```shell
sh scripts/run.sh
```
All hyper-parameter configurations are provided in `configuration` folder and `train_forecasting.py` file (default configurations). 