locussam/t2vec

This repository contains the code used in our ICDE-18 paper Deep Representation Learning for Trajectory Similarity Computation.

Requirements

Ubuntu OS
Julia 0.6.4 (Julia 0.7+ is untested)
Python >= 3.5 (Anaconda3 is recommended)
PyTorch 0.1.2 (You may want to use virtualenv to avoid being conflict with your current version)

Please refer to the source code to install all required packages in Julia and Python.

In Julia, you can install a package in REPL like

               _
   _       _ _(_)_     |  A fresh approach to technical computing
  (_)     | (_) (_)    |  Documentation: https://docs.julialang.org
   _ _   _| |_  __ _   |  Type "?help" for help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 0.6.4 (2018-07-09 19:09 UTC)
 _/ |\__'_|_|_|\__'_|  |  Official http://julialang.org/ release
|__/                   |  x86_64-pc-linux-gnu


julia> Pkg.add("StatsBase")

Preprocessing

The preprocessing step will generate all data required in the training stage.

Two parameters can be set at this step, cellsize in preprocessing/preprocess.jl and denoising radius in preprocessing/utils.jl:disort(), you can leave them as their default values which are the ones used in our paper.

$ curl http://archive.ics.uci.edu/ml/machine-learning-databases/00339/train.csv.zip -o data/porto.csv.zip
$ unzip data/porto.csv.zip
$ mv train.csv data/porto.csv
$ cd preprocessing
$ julia preprocess.jl

The generated files for training are saved in data/.

Training

$ python t2vec.py -data data -vocab_size 18866 -criterion_name "KLDIV" -knearestvocabs "data/porto-vocab-dist-cell100.h5"

The training produces two model checkpoint.pt and best_model.pt, checkpoint.pt contains the latest trained model and best_model.pt saves the model which has the best performance on the validation data. You can find our saved best_model.pt here.

In our original experiment, the model was trained with a Tesla K40 GPU about 14 hours so you can just terminate the training after 14 hours if you use a GPU that is as good as or better than K40, the above two models will be saved automatically.

Embedding

Create test files

cd experiment

julia createTest.jl

head -5 trj.t # the first 5 trajectories
head -5 trj.label # trajectory ids

It will produce two files trj.t and trj.label. Each row of trj.t (trj.label) is a token representation of the orginal trajectory (trajectory ID).

Embed trajectories into vectors

$ python t2vec.py -data experiment -vocab_size 18866 -checkpoint "best_model.pt" -mode 2

It will encode the trajectories in file experiment/trj.t into vectors which will be saved into file experiment/trj.h5.

Vector representation

In our experiment we train a three-layers model and the last layer outputs are used as the trajectory representations, see the code in experiment/experiment.jl:

vecs = h5open(joinpath("", "trj.h5"), "r") do f
    read(f["layer3"])
end

vecs[i] # the vector representation of i-th trajectory

Reference

@inproceedings{DBLP:conf/icde/LiZCJW18,
  author    = {Xiucheng Li and
               Kaiqi Zhao and
               Gao Cong and
               Christian S. Jensen and
               Wei Wei},
  title     = {Deep Representation Learning for Trajectory Similarity Computation},
  booktitle = {34th {IEEE} International Conference on Data Engineering, {ICDE} 2018,
               Paris, France, April 16-19, 2018},
  pages     = {617--628},
  year      = {2018},
  crossref  = {DBLP:conf/icde/2018},
  url       = {https://doi.org/10.1109/ICDE.2018.00062},
  doi       = {10.1109/ICDE.2018.00062},
  timestamp = {Tue, 20 Nov 2018 10:20:00 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/icde/LiZCJW18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

locussam / t2vec