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xairq

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    Clara Betancourt authored
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    XAirQ

    Explainable machine learning for air quality

    Structure of this repository

    • The root directory contains prepare.sh, LICENSE.md, .gitignore etc.
    • setup contains requirement files and hpc modules
    • source contains all python scripts, ordered by topic (preprocessing, models...)
    • test contains tests for the python scripts
    • resources contain descriptive .csv files needed for analysis
    • jupyter contains jupyter notebooks for full workflows
    • doc contains documentation materials

    Prerequisites

    • Python Version >=3.8 with virtualenv package
    • Debian packages: PROJ and GEOS, graphviz. Install on Ubuntu 20.04 with sudo apt-get install libproj-dev proj-data proj-bin; sudo apt-get install libgeos-dev sudo apt install graphviz. If you use a conda distribution, these packages should already be installed.

    Getting started on own machine

    • Clone the repository to JUWELS or your own linux machine
    • Run source prepare.sh to activate the python environment
    • Run ./run.sh to start scripts. Currently, only the option "test" is available here. in source/models/, there are some models you can run, e.g. with python random_forest.py

    Code style

    We try to write in the pep8 style!

    • Take a look at the pep8 convention
    • Start the analysis with ./run.sh and choose the pep8 option
    • Enter your name, so all the scripts you wrote are checked
    • Be sure to state an __author__ in the scripts you wish to check.

    Getting started on JUWELS

    • Clone the repository to JUWELS
    • Run source prepare.sh to create the python HPC environment for JUWELS
    • Go to /ozone-interpolation/hpc_scripts and sbatch train_xxx_.sh for submitting jobs (currently only Random Forest and Neural Network)

    Authors

    • Scarlet Stadtler
    • Clara Betancourt

    License

    This project is licensend under MIT License.