cs224w-traffic

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Course project for cs224w

cs224w-traffic 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Network-based traffic speed prediction

![Diffusion Convolutional Recurrent Neural Network](https://raw.githubusercontent.com/syin3/cs224w-traffic/master/figures/model-structure.png "Model Architecture")

This project inherits the main structure of DCRNN described in the *Li et al, ICLR 2018* paper. In general, we discuss how much pre-knowledge of distances is needed. The edge weights of adjacency matrices are

+ Distance-smoothed as in paper
    + Binary values indicating connectivity

We then further discuss repectively in the two cases:
    
    + How fully-connected RNN cells with graph embedding knowledge compare to DCRNN 1-hop and 2-hop models?
    + Will attention mechanism, which is added to the diffusion process, learn meaningful attention weights, e.g. mapping from distance?

## 1, Graph Construction
Current implementation currently only supports sensor ids in Los Angeles (see `data/sensor_graph/sensor_info_201206.csv`). We prepare distance-smoothed graph edge weight matrix as follows:

```bash
python -m scripts.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_la.pkl.pkl
```
and plain adjancency matrix (binary indicating connectivity) as

```bash
python scripts.gen_adj_binary.py  --sensor_ids_filename=data/sensor_graph/graph_sensor_ids.txt --output_pkl_filename=data/sensor_graph/adj_bin_la.pkl.pkl
```

In file `/figures/ProduceEdgeList.ipynb`, we inspect the dimension, directedness and other features of the adjacency matrix of LA highway sensor system.

## 2, Traffic 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/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).
```bash
python -m scripts.generate_training_data --output_dir=data/METR-LA
```
The generated train/val/test dataset will be saved at files `data/METR-LA/{train,val,test}.npz`.

## 3, Graph Embedding Data Preparation
Follow the instructions in file `embeddings.ipynb`, we produce **node2vec**graph embeddings of designated dimension, which later is attached to the input feature matrix and is then fed to the fully connected neural network.

Activate Python2 environment for node2vec by `source activate cs224w`;
Then either generate a single node2vec by
`python n2v-main.py --input METR-LA.txt --output LA-n2v-temp.txt --dimensions 8 --p 1 --weighted --directed`
or by `sh generate-hyperparam-grid.sh` to generate files with hyperparameters from grid search.

Before you proceed, please make sure embeddings files are in ```bash data/embeddings``` directory and traffic data are correctly generated in ```bash data/METR-LA``` directory.

## 4, Model Training

We talk about training in one GPU.

The arguments fed into **dcrnn_train.py** are dependent as follows:

+ **--config_filename**: the configuration file where hyperparameters are recorded;
+ **--use_cpu_only**: flag to use cpu only for training;
+ **--weightType**: distance-smoothed or binary adjancency;
+ **--att**: flag to use attention;
+ **--no-att**: flag not to use attention;
+ **fc**: flag to use fully connected cells
    - **--gEmbedFile**: if  **fc**, need to specify which embedding file to use;
+ **graphConv**: flag to use graph convolution
    - **--hop**: if **graphConv**, need to specify hop of neighborhood;

Therefore, this is a combination of {distance-aware, binary}, {fully connected, graph convolution}, and {attention, no-attention}.

You could do these for distance-aware training
```bash
python dcrnn_train.py --config_filename data/model/dcrnn_la.yaml --weightType d --no-att graphConv --hop 1
python dcrnn_train.py --config_filename data/model/dcrnn_la.yaml --weightType d --no-att fc --gEmbedFile LA-n2v-14-0.1-1
```
and these for binary

```bash
python dcrnn_train.py --config_filename data/model/dcrnn_la.yaml --weightType a --no-att graphConv --hop 2
python dcrnn_train.py --config_filename data/model/dcrnn_la.yaml --weightType a --no-att fc --gEmbedFile LA-n2v-14-0.1-1
```

Each epoch takes about 5min with a single GTX 1080 Ti (on DCRNN).

## 5, Model evaluation

The preferred way is to actually make use of the log information.

## 6, Visualization

See `/figures/plot.ipynb` for the visualization of results.

## 7, Please cite original paper as specified by authors

If you find this repository useful in your research, please cite the following papers:
```
@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}
}

@inproceedings{grover2016node2vec,
title={node2vec: Scalable feature learning for networks},
author={Grover, Aditya and Leskovec, Jure},
booktitle={Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining},
pages={855--864},
year={2016},
organization={ACM}
}

@inproceedings{wang2016structural,
title={Structural deep network embedding},
author={Wang, Daixin and Cui, Peng and Zhu, Wenwu},
booktitle={Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining},
pages={1225--1234},
year={2016},
organization={ACM}
}
```

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