PyTorch implementation of WWW'18 paper-DeepMove: Predicting Human Mobility with Attentional Recurrent Networks link
The sample data to evaluate our model can be found in the data folder, which contains 800+ users and ready for directly used. The raw mobility data similar to ours used in the paper can be found in this public link.
- Python 2.7
- Pytorch 0.20
cPickle is used in the project to store the preprocessed data and parameters. While appearing some warnings, pytorch 0.3.0 can also be used.
- /codes
- main.py
- model.py # define models
- sparse_traces.py # foursquare data preprocessing
- train.py # define tools for train the model
- /pretrain
- /data # preprocessed foursquare sample data (pickle file)
- /docs # paper and presentation file
- /resutls # the default save path when training the model
- Load a pretrained model:
python main.py --model_mode=attn_avg_long_user --pretrain=1
The codes contain four network model (simple, simple_long, attn_avg_long_user, attn_local_long) and a baseline model (Markov). The parameter settings for these model can refer to their res.txt file.
| model_in_code | model_in_paper | top-1 accuracy (pre-trained) |
|---|---|---|
| markov | markov | 0.082 |
| simple | RNN-short | 0.096 |
| simple_long | RNN-long | 0.118 |
| attn_avg_long_user | Ours attn-1 | 0.133 |
| attn_local_long | Ours attn-2 | 0.145 |
- Train a new model:
python main.py --model_mode=attn_avg_long_user --pretrain=0
Other parameters (refer to main.py):
- for training:
- learning_rate, lr_step, lr_decay, L2, clip, epoch_max, dropout_p
- model definition:
- loc_emb_size, uid_emb_size, tim_emb_size, hidden_size, rnn_type, attn_type
- history_mode: avg, avg, whole
Batch version for this project will come soon.