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DCRNN
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Update README and requirements.

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Yaguang 2018-09-30 22:15:27 -07:00
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README.md
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@ -9,7 +9,6 @@ Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent
## Requirements
- hyperopt>=0.1
- scipy>=0.19.0
- numpy>=1.12.1
- pandas>=0.19.2
@ -22,25 +21,14 @@ Dependency can be installed using the following command:
pip install -r requirements.txt
```
## Traffic Data
## Data Preparation
The traffic data file for Los Angeles, i.e., `df_highway_2012_4mon_sample.h5`, is available [here](https://drive.google.com/open?id=1tjf5aXCgUoimvADyxKqb-YUlxP8O46pb), and should be
put into the `data/` folder.
put into the `data/METR-LA` folder.
Besides, the locations of sensors are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).
## 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 gen_adj_mx.py --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
--output_pkl_filename=data/sensor_graph/adj_mx.pkl
```
## Train the Model
```bash
python dcrnn_train.py --config_filename=data/model/dcrnn_config.yaml
python -m scripts.generate_training_data --output_dir=data/METR-LA
```
The generated train/val/test dataset will be saved at `data/METR-LA/{train,val,test}.npz`.
## Run the Pre-trained Model
@ -51,6 +39,21 @@ python run_demo.py
The generated prediction of DCRNN is in `data/results/dcrnn_predictions_[1-12].h5`.
## Model Training
```bash
python dcrnn_train.py --config_filename=data/model/dcrnn_config.yaml
```
Each epoch takes about 5min with a single GTX 1080 Ti. There is a chance that train/val loss will explode, gradient explosion,
## 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 gen_adj_mx.py --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --normalized_k=0.1\
--output_pkl_filename=data/sensor_graph/adj_mx.pkl
```
More details are being added ...
## Citation

1
requirements.txt
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@ -1,4 +1,3 @@
hyperopt>=0.1
scipy>=0.19.0
numpy>=1.12.1
pandas>=0.19.2