Raise red flags on machine learning datasets.
Project description
redflag
Automatic safety net for machine learning datasets.
Installation
You can install this package with pip:
pip install redflag
Experimental
Installing scikit-learn allows you to access some extra options for outlier detection.
pip install redflag[sklearn]
Example
Coming soon.
Testing
You can run the tests (requires pytest and pytest-cov) with
python run_tests.py
Building
This repo uses PEP 517-style packaging. Read more about this and about Python packaging in general.
Building the project requires build, so first:
pip install build
Then to build redflag locally:
python -m build
The builds both .tar.gz and .whl files, either of which you can install with pip.
Continuous integration
This repo has two GitHub 'workflows' or 'actions':
- Push to
main: Run all tests on all version of Python. This is the Run tests workflow. - Publish a new release: Build and upload to PyPI. This is the Publish to PyPI workflow. Publish using the GitHub interface, for example (read more
© 2021 Agile Scientific, openly licenced under Apache 2.0
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