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data's Introduction

Trajectory Mining Library

CircleCI_ Release_ Python_ License_ CodeQuality_

Trajectory mining library inspired by and based on scikit-learn.

Installation

To install trajminer using pip, run: :

pip install trajminer
Trajminer requires Python 3.6.5 or greater, plus the following packages:
  • joblib >= 0.13.0
  • numpy >= 1.15.3
  • scikit-learn >= 0.19.1
  • keras >= 2.2.4
  • geohash2 >= 1.1
  • pandas >= 0.24.1

Documentation

The documentation of the latest version, as well as of past versions, can be found here.

Contribute

Everyone is welcome to contribute to the project, either by fixing open issues or suggesting new features and reporting bugs (be sure your issue isn't already reported or even fixed).

Testing

pytest trajminer

Checking Code Style (PEP8)

bash check_code_style.sh

Building the Docs

Inside the doc folder, run: :

bash build.sh [target-folder]

Some Guidelines

Before submitting a pull request:

  • Please check if your code follows the PEP8 style guide. You may do so by running flake8.
  • Make sure you added the proper documentation (if adding new features) or corrected the existing one (if making changes to existing code). Also, ensure that you can build the docs.
  • Please ensure that all tests are passing.

Citation in Scientific Works

If you use this library in any scientific work, please cite it as follows: :

@misc{petry2019trajminer,
  title={Trajminer},
  author={Petry, Lucas May and others},
  year={2019},
  howpublished={\url{https://trajminer.github.io}},
}

data's People

Contributors

falcaopetri avatar lucaspetry avatar

Stargazers

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Watchers

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Forkers

falcaopetri

data's Issues

Foursquare dataset and preprocessing script

I created PR #1 to add a script that gets the original Foursquare datasets (from here) and preprocess them to generate the datasets in this repo (added in commit b19b3f0).

While testing, I noticed that although the script output was exactly equal to the data in this repo, the data itself was not exactly equal to the original.

Then, I verified that numerical rounding issues are introduced during the reading/writing process. I suppose it doesn't make any difference, but it may be good to be aware of this.

The following is a side-by-side diff of a subset of the values:

Original NYC values Preprocessed data values
40.60679958140643 -74.04416981025437 40.606799581406435 -74.04416981025437
40.716161684843215 -73.88307005845945 40.71616168484322 -73.88307005845945
40.7451638 -73.982518775 40.7451638 -73.982518775
40.74010382743943 -73.98965835571289 40.74010382743943 -73.98965835571289
40.69042711809854 -73.95468677509598 40.69042711809854 -73.954686775096
40.751591431346306 -73.9741214009634 40.75159143134631 -73.9741214009634
40.77942173066975 -73.95534113280371 40.77942173066975 -73.95534113280371
40.61915106755737 -74.03588760058483 40.61915106755737 -74.03588760058483
40.61900594093755 -73.99037472596906 40.61900594093755 -73.99037472596906

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