Guarding OSM from invalid or suspicious edits!
Project description
EXPERIMENTAL: UNDER DEVELOPMENT
Guarding OSM from invalid or suspicious edits, Gabbar is an alpha package of a pre-trained binary problematic/not problematic classifier that was trained on manually labelled changesets from OpenStreetMap.
https://en.wikipedia.org/wiki/Gabbar_Singh_(character)
Install
pip install gabbar
Running tests
# Setup a virtual environment.
$ mkvirtualenv gabbar
# Install in locally editable (``-e``) mode.
$ pip install -e .
# Run the tests.
$ py.test
Publishing to PyPi
# Bump the version.
$ $EDITOR setup.py
# Bump the tag.
$ git tag <VERSION>
# Push your changes to Github.
$ git push
$ git push --tags
# Create a Source Distribution.
python setup.py sdist
# A wheel can be installed without needing to go through the "build" process.
python setup.py bdist_wheel --universal
# Optionally upload to Test PyPI if required.
$ twine upload dist/* -r testpypi
# Upload to PyPi
twine upload dist/*
Model training
# Download changesets checked on osmcha.
wget -O training/changesets.csv https://www.dropbox.com/s/o05zxyhgkt8j4mx/changesets-checked-2017-02-17.csv?dl=1
# Train a machine learning model.
$ python training/train-model.py
precision recall f1-score support
problematic 0.91 0.02 0.04 1406
not problematic 0.88 1.00 0.94 10249
avg / total 0.88 0.88 0.83 11655
# Find the trained model as a .pkl file.
$ ls training/gabbar.pkl
training/gabbar.pkl
# Test model for a problematic edit prediction.
$ python training/test-model.py
Hyperlinks
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