Synthetic location trajectories generation using categorical diffusion models

About

This repository contains library code for training a categorical diffusion probabilistic model for generation of synthetic location trajectories.

Installation

To install the latest GitHub , just call the following on the command line:

docker build https://github.com/irmlma/ mobility-simulation-cdpm.git#<TAG> -t dmma

where is, e.g., v0.1.0.

Example usage

Having installed as described above you can train and generate trajectories using the provided Docker image.

First test the container using:

docker run dmma --help

Train the model using the provided config file via: dock

docker run -v <<some path>>:/mnt \
  dmma /
  --mode=train \
  --config=/mnt/<<config.py>> \
  --infile=/mnt/<<train_dataset.csv>> \
  --outfile=/mnt/<<outfile.pkl>>

where

• <<some path> is a local path you want to mount to /mnt/ to make it accessible to Docker,
• <<config.py>> is a config file that is following the template in configs/config.py,
• <<train_dataset.csv>> is a comma-separated file of numerical values which correspond to the features obtained from transforming inputs through a neural network,
• <<outfile.pkl>> is the outfile to which parameter and meta data is saved.

To simulate some data, use

docker run -v <<some path>>:/mnt \
  --mode=simulate \
  --config=/mnt/<<config.py>> \
  --outfile=/mnt/<<outfile.csv>> \
  --checkpoint=/mnt/<<checkpoint>> \
  --n_seqs=10 \
  --len_seqs=32

where

• <<some path> is the same as above,
• <<config.py>> is the same as above,
• <<test_dataset.csv>> is a data set for which you want to evaluate if it is OoD,
• <<outfile.pkl>> is the name of the outfile,
• <<checkpoint>> is the parameter file obtained through the training (i.e., in this case <<outfolder>>/params.pkl),
• n_seqs determines the number of generated location trajectories,
• len_seqs is the length of each trajectory.

Citation

If you find our work relevant to your research, please consider citing

@article{dirmeier2024cdpm,
  title={Synthetic location trajectories generation using categorical diffusion models},
  author={Simon Dirmeier and Ye Hong and Fernando Perez-Cruz},
  year={2024},
  journal={arXiv preprint arXiv:2402.12242}
}

Author

Simon Dirmeier sfyrbnd @ pm me