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
Setup
# Setup a virtual environment with Python 3.
mkvirtualenv --python=$(which python3) gabbar_py3
# Install in locally editable (``-e``) mode.
pip install -e .[test]
# Install node dependencies.
npm install
Get a prediction
# Get a prediction for a changeset.
$ gabbar 47734592
{"prediction": "good", "timestamp": "2017-04-26 01:05:00.441977", "version": "0.2.4"}
Run tests
# Run tests.
npm run test
Performance
Performance of the model is tracked in metrics.csv
Real-world performance is tracked in performance.md
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