ASTGCN-中的代码---1个Feature

# ASTGCN Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) <img src="fig/ASTGCN architecture.png" alt="image-20200103164326338" style="zoom:50%;" /> This is a Pytorch implementation of ASTGCN and MSTCGN. The pytorch version of ASTGCN released here only consists of the recent component, since the other two components have the same network architecture. # Reference ```latex @inproceedings{guo2019attention, title={Attention based spatial-temporal graph convolutional networks for traffic flow forecasting}, author={Guo, Shengnan and Lin, Youfang and Feng, Ning and Song, Chao and Wan, Huaiyu}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={33}, pages={922--929}, year={2019} } ``` # Configuration Step 1: The loss function and metrics can be set in the configuration file in ./configurations Step 2: The last three lines of the configuration file are as follows: ```c++ loss_function = masked_mae metric_method = mask missing_value = 0.0 ``` loss_function can choose 'masked_mae', 'masked_mse', 'mae', 'mse'. The loss function with a mask does not consider missing values. metric_method can choose 'mask', 'unmask'. The metric with a mask does not evaluate missing values. The missing_value is the missing identification, whose default value is 0.0 # Datasets Step 1: Download PEMS04 and PEMS08 datasets provided by [ASTGNN](https://github.com/guoshnBJTU/ASTGNN/tree/main/data). Step 2: Process dataset - on PEMS04 dataset ```shell python prepareData.py --config configurations/PEMS04_astgcn.conf ``` - on PEMS08 dataset ```shell python prepareData.py --config configurations/PEMS08_astgcn.conf ``` # Train and Test - on PEMS04 dataset ```shell python train_ASTGCN_r.py --config configurations/PEMS04_astgcn.conf ``` - on PEMS08 dataset ```shell python train_ASTGCN_r.py --config configurations/PEMS08_astgcn.conf ```