Machine Learning Libraries You'd Wish You'd Known About

Given to London Python in 2018-01.

Transcript

1. Machine learning libraries you'd wish you'd known about London Python 2018-01 Ian Ozsvald @IanOzsvald ModelInsight.io
2. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Introductions • I’m an engineering data scientist • Consulting in AI + Data Science for 15+ years Blog->IanOzsvald.com
3. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Goals today • Can I calculate on Pandas in parallel? • Can I automate my machine learning? • Is my regression working? • Why did it make that decision? • Github for examples: Builds on PyConUK 2016 – my introduction to Random Forests as a worked process with examples and graphs
4. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 watermark for reproducibility
5. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Dask for Medium Data Tasks • Pandas-compatible parallel processor • Also see: Automated data exploration by Víctor Zabalza at PyConUK 2017 • http://ianozsvald.com/2017/06/07/kaggles -quora-question-paris-competition/ • In top 40% in < 6 days of effort
6. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Dask – 3.5* speedup
7. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 TPOT – automated ML
8. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 TPOT • Used on Kaggle Mercedes (6 week competition, 5 days of my effort) • In top 40% result with little more than TPOT and a few days • Ensembled 3 estimators (2 from TPOT) • http://ianozsvald.com/2017/07/01/kaggl es-mercedes-benz-greener-manufacturing
9. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 pandas_profiling for EDA
10. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Why explain our models? • Check that our model works as we’d expect in the real world – are the “important features” really important? Are they noise? • Help colleagues gain confidence in the model • Diagnose if certain examples are poorly understood
11. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Boston housing data • Regress median-value (MEDV) from other features • LSTAT - ‘low status %’ • RM - ‘median rooms’ • 13 features overall • 506 rows
12. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Yellowbrick • Lots of visualisations that plug into sklearn • Classification – class balance, confusion matrix • Regression – y vs ŷ, residual errors • Presented at PyDataLondon 2017 • http://www.scikit-yb.org/en/latest/
13. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Yellowbrick
14. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 ELI5 • “Explain it like I’m 5!” • Feature Importance via Permutation Importance • Prediction explanations including text • Sklearn, XGBoost, LightGBM • http://eli5.readthedocs.io/en/latest/
15. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 ELI5 - Permutation Importance • Model agnostic, hopefully not skewed • Useful with both RF and linear models RandomForest's feature importances:
16. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Explaining the regression • ELI5 & LIME can explain single examples • Expensive house – many rooms, low LSTAT %, good pupil/teacher ratio • Cheap house – high LSTAT %, few rooms, maybe high nitric oxide pollution and lower pupil/teacher ratio • These interpretations are different to the global feature importances • Also see Kat Jarmul’s keynote @ PyDataWarsaw 2017: https://blog.kjamistan.com/towards-interpretable-reliable-models • Michał Łopuszyński @ PyDataWarsaw https://www.slideshare.net/lopusz/debugging-machinelearning
17. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 ELI5 explanation • Model specific • Explain “46.8” • Expensive property • RM & LSTAT • Some PTRATIO
18. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 ELI5 explain many examples
19. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 ELI5 explain many examples Few rooms, close to employment centres, lower LSTAT% Many rooms (big houses!)
20. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 LIME (circa 2016) • Locally linear classifiers built around the 1 data point you want to explain • Model agnostic, even images & text! • https://github.com/marcotcr/lime
21. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 LIME • 10.99 predicted (cheap property) • Strong negative influences (from the mean price) – LSTAT, RM, NOX, ... Caveats: http://eli5.readthedocs.io/en/latest/blackbox/lime.html
22. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Closing... • Diagnose your ML just like you debug your code – explain its working to colleagues • Write-up: http://ianozsvald.com/ • Data science team coaching – can I help? • Questions in exchange for beer :-) • Learn something? Please send me a postcard! • See my longer diagnosis Notebook on github:
23. Ian.Ozsvald@ModelInsight.io @IanOzsvald[.com] London Python 2018-01 Appendix: Dask – 3.5* speedup https://twitter.com/ianozsvald/status/870643737097056259