# DCRNN_PyTorch

**Repository Path**: chen_guo_qi/DCRNN_PyTorch

## Basic Information

- **Project Name**: DCRNN_PyTorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: pytorch_scratch
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2020-09-24
- **Last Updated**: 2023-12-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

![Diffusion Convolutional Recurrent Neural Network](figures/model_architecture.jpg "Model Architecture")

This is a PyTorch implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926), ICLR 2018.


## Requirements
* torch
* scipy>=0.19.0
* numpy>=1.12.1
* pandas>=0.19.2
* pyyaml
* statsmodels
* tensorflow>=1.3.0
* torch
* tables
* future

Dependency can be installed using the following command:
```bash
pip install -r requirements.txt
```

### Comparison with Tensorflow implementation

In MAE (For LA dataset, PEMS-BAY coming in a while)

| Horizon | Tensorflow | Pytorch |
|:--------|:--------:|:--------:|
| 1 Hour |   3.69   |   3.12   |    
| 30 Min |   3.15   |   2.82   |    
| 15 Min |   2.77   |   2.56   |    


## Data Preparation
The traffic data files for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY), i.e., `metr-la.h5` and `pems-bay.h5`, are available at [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g), and should be
put into the `data/` folder.
The `*.h5` files store the data in `panads.DataFrame` using the `HDF5` file format. Here is an example:

|                     | sensor_0 | sensor_1 | sensor_2 | sensor_n |
|:-------------------:|:--------:|:--------:|:--------:|:--------:|
| 2018/01/01 00:00:00 |   60.0   |   65.0   |   70.0   |    ...   |
| 2018/01/01 00:05:00 |   61.0   |   64.0   |   65.0   |    ...   |
| 2018/01/01 00:10:00 |   63.0   |   65.0   |   60.0   |    ...   |
|         ...         |    ...   |    ...   |    ...   |    ...   |


Here is an article about [Using HDF5 with Python](https://medium.com/@jerilkuriakose/using-hdf5-with-python-6c5242d08773).

Run the following commands to generate train/test/val dataset at  `data/{METR-LA,PEMS-BAY}/{train,val,test}.npz`.
```bash
# Create data directories
mkdir -p data/{METR-LA,PEMS-BAY}

# METR-LA
python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5

# PEMS-BAY
python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
```

## Graph Construction
 As the currently implementation is based on pre-calculated road network distances between sensors, it currently only
 supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`).
```bash
python -m scripts.gen_adj_mx  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
    --output_pkl_filename=data/sensor_graph/adj_mx.pkl
```
Besides, the locations of sensors in Los Angeles, i.e., METR-LA, are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).

## Run the Pre-trained Model on METR-LA

```bash
# METR-LA
python run_demo_pytorch.py --config_filename=data/model/pretrained/METR-LA/config.yaml

# PEMS-BAY
python run_demo_pytorch.py --config_filename=data/model/pretrained/PEMS-BAY/config.yaml
```
The generated prediction of DCRNN is in `data/results/dcrnn_predictions`.


## Model Training
```bash
# METR-LA
python dcrnn_train_pytorch.py --config_filename=data/model/dcrnn_la.yaml

# PEMS-BAY
python dcrnn_train_pytorch.py --config_filename=data/model/dcrnn_bay.yaml
```

There is a chance that the training loss will explode, the temporary workaround is to restart from the last saved model before the explosion, or to decrease the learning rate earlier in the learning rate schedule. 


## Eval baseline methods
```bash
# METR-LA
python -m scripts.eval_baseline_methods --traffic_reading_filename=data/metr-la.h5
```

### PyTorch Results

![PyTorch Results](figures/result1.png "PyTorch Results")

![PyTorch Results](figures/result2.png "PyTorch Results")

![PyTorch Results](figures/result3.png "PyTorch Results")

![PyTorch Results](figures/result4.png "PyTorch Results")

## Citation

If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:
```
@inproceedings{li2018dcrnn_traffic,
  title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting},
  author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan},
  booktitle={International Conference on Learning Representations (ICLR '18)},
  year={2018}
}
```