Source code of the paper Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
If you find this repository useful in your research, please cite the following paper:
@inproceedings{yao2019metast,
title={Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction},
author={Yao, Huaxiu and Liu, Yiding and Wei, Ying and Tang, Xianfeng and Li, Zhenhui},
booktitle={the Web Conference 2019 (WWW'19)},
year={2019}
}
- Taxi Data
- NYC, Washington DC, Porto, Chicago, Boston
- processed data is in './data/taxi'
- Bike Data
- NYC, Washington DC, Chicago
- processed data is in './data/bike'
- Environment PH Data
- Midwest, Northeast, Pacific, South, Southwest, West
- processed data is in './data/environment'
Please check the data and scripts/preprocessing, training and testing for more details.
First, construct folders named models, outputs, test_data
This part is used to generate the sequential data for training.
python ./maml/preprocess.py --filename=/A/B.npz --cluster_file=/cluster/A/cluster_B --save_filename=B_seq.npz
A can be replaced by the task (taxi, bike, environment), B can be replaced by the city (e.g., nyc, dc)
For training, please use:
python ./maml/train_model.py --cities=several cities --save_dir=./models --model_type=att_metatrain_mem8 --update_batch_size=128 --test_num_updates=5 --threshold=0 --mem_dim=8 --cluster_loss_weight=1e-4 --meta_lr=1e-5 --update_lr=1e-5 --iterations=20000 --gpu_id=0
For testing, please use:
python ./maml/test_model.py --city=chicago --save_dir=./models --output_dir=./outputs --model_type=att_metatrain_mem8 --test_model=model_3200 --test_days=3 --update_batch_size=128 --threshold=0 --meta_lr=1e-5 --update_lr=1e-5 --epochs=30 --gpu_id=0
Finally, run analysis.py to get denormalized results