gabbar 0.2.4

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 osm changeset dataset and split into good and problematic changeset files.
$ python training/dataprep.py

# View features of changeset used for training the model.
$ head -n5 training/problematic.prep.csv
check date,create,modify,delete
2016-12-20T19:55:46.153444+00:00,0.0,0.0,327.0
2016-12-20T20:43:03.137419+00:00,9062.0,0.0,0.0
2016-12-20T12:09:43.259591+00:00,10.0,20.0,8.0
2016-12-21T10:09:33.445841+00:00,337.0,538.0,113.0

# Run datatrain.py to train a OneClassSVM on the dataset.
$ python training/datatrain.py
training samples: 12364
[testing] good samples: 5299
[testing] problematic samples: 671
precision = 0.915625
recall = 0.442348
f1_score = 0.596514

# Find the trained model as a .pkl file.
$ ls training/gabbar.pkl
training/gabbar.pkl

Hyperlinks

• Validating and protecting OpenStreetMap

• An open database of inconsistent edits observed on OSM from OSMCha