Skip to content

liuyiding1993/ICDE2020_GMVSAE

Repository files navigation

Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling (ICDE 2020)

How To Use (the new version)

Preprocessing

The processed data files (i.e., processed_porto_train.csv and processed_porto_val.csv) will be put in ./data.

Training

Example of training on Porto dataset:

python run_loop.py --mode=train --cluster_num=5 --num_epochs=5 --gpu_id=0 \ 
                   --model_dir=./ckpt --learning_rate=1e-4 --num_epochs=10 --pretrain_dir=./pretrain

More conveniently, we can run pretraining, training and evaluation via pretrain.sh, train.sh and eval.sh, respectively.

Parameters:

Name Type Description
mode enum(str) pretrain, train or evaluate.
data_filename str data file (e.g., ./data/processed_porto.csv).
map_size (int, int) size of the grid map.
token_dim int dimensionality of grid token.
rnn_dim int dimensionality of rnn hidden state.
cluster_num int number of Gaussian components.
model_dir str directory to save/load a model during training or eval.
pretrain_dir str directory to save/load a model during pretraining.
num_negs int number of negative samples during training.
optimizer enum(str) training optimizer (e.g., adam or sgd).
learning_rate float learning rate for training.
num_epochs int number of passes over the training data.
log_steps int number of batches to print the log info.

Citation

Please kindly cite the paper if this repo is helpful :)

@inproceedings{liu2020online,
  title={Online anomalous trajectory detection with deep generative sequence modeling},
  author={Liu, Yiding and Zhao, Kaiqi and Cong, Gao and Bao, Zhifeng},
  booktitle={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
  pages={949--960},
  year={2020},
  organization={IEEE}
}

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages