A library of tools for easier evaluation of ML models.
Project description
MLLytics
Installation instructions
pip install MLLytics
or
python setup.py install
Update pypi instructions (for me)
Creates the package
python setup.py sdist bdist_wheel
Upload package
twine upload --repository pypi *version_files*
Future
Improvements and cleanup
- Allow figure size and font sizes to be passed into plotting functions
- Comment all functions and classes
- Add type hinting to all functions and classes (https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html)
- Example guides for each function in jupyter notebooks
Cosmetic
- Fix size of confusion matrix
- Check works with matplotlib 3
- Tidy up legends and annotation text on plots
- Joy plots
- Brier score for calibration plot
- Tidy up cross validation and plots (also repeated cross-validation)
Big push
- Scoring functions
- MultiClassMetrics class to inherit from ClassMetrics and share common functions
- More output stats in overviews
- Update reliability plot https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/
- Tests
- Switch from my metrics to sklearn metrics where it makes sense? aka
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
and more general macro/micro average metrics from: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
Contributing Authors
- Scott Clay
- David Sullivan
Project details
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