Pydata BLN 2018: Five things I learned while prototyping ML papers

https://pydata.org/berlin2018/schedule/presentation/40/

Transcript

1. FIVE THINGS I LEARNED WHILE PROTOTYPING ML PAPERS ELLEN KÖNIG / @ELLEN_KOENIG SENIOR DATA SCIENTIST NATIVE INSTRUMENTS
2. A LONG, LONG TIME AGO… IN AN OFFICE FAR AWAY…
3. HOW MANY OF YOU CAN RELATE TO OUR PROBLEM?
5. BUT WORK IS ALL ABOUT GROWTH, RIGHT??
6. FORTUNATELY
7. OUR USE CASE
8. WHEN SHOULD YOU LOOK FOR RESEARCH PAPERS? • „Somebody must have solved this before!“ • No ready-to-use implementation
9. A WORKFLOW FOR PROTOTYPING ML PAPERS 1. Search for research findings 2. Decide on comparison criteria 3. Evaluate your papers 4. Prioritize approaches 5. Prototype approaches
10. STEP 1: SEARCH FOR RESEARCH FINDINGS Needed: An overview of the field
11. COMPILING AN OVERVIEW OF THE FIELD Compile Foundational and cutting edge papers Problems and approaches Start with survey papers, follow references
12. STEP 2: DECIDE ON YOUR COMPARISON CRITERIA
13. WHICH PAPERS ARE RIGHT FOR YOU? Summarize common metrics and baselines Refresher on baselines: https://www.quora.com/What-does- baseline-mean-in-machine-learning Pick simple metrics and baselines Minimally required metric targets?
14. STEP 3: EVALUATE YOUR PAPERS Groundbreaking? Copycat? Garbage? Journal / conference quality? Team experience?
15. STEP 3: EVALUATE YOUR PAPERS — A CHECKLIST 3. Results 2. Methodology 1. Abstract & Introduction
16. ABSTRACT & INTRODUCTION Addresses your problem? Similar context? Approach: Groundbreaking or improvement? Results: Better than targets & baseline? Main question: Relevant to your problem? 3. Results 2. Methodology ✔Abstract & Introduction
17. METHODOLOGY SECTION Main question: Approach reproducible? Data set size and content similar? ✓22k black-and-white pages ✓German corpus ? Research documents rather than banking documents 3. Results ✔ Methodology ✔ Abstract & Introduction
18. METHODOLOGY SECTION Entire process described? ✓Seems to be complete Pre-processing steps described completely? ✓Image conversion and scaling is described ? OCR tool / approach is not mentioned Well-known methods? Or completely described methods? ✓Neural network with descriptions of the configuration 3. Results ✔ Methodology ✔ Abstract & Introduction
19. RESULTS SECTION Main question: Results reliable? Relevant metrics for your use case? ✓Accuracy Metrics appropriate for the problem? ✓Common metric for classification Metrics appropriate for the dataset? XNot suitable for imbalanced classes ✔ Results ✔ Methodology ✔ Abstract & Introduction
20. RESULTS SECTION Better than your baseline? ✓Yes, by 0.23 over the baseline Better than the metrics target? ? They are close Any published review of the results? ? Not yet Improvement analyzed with suitable statistical tests? X No statistical analysis, and reported measurements are not comparable ✔ Results ✔ Methodology ✔ Abstract & Introduction
21. RESULTS SECTION • For a refreshers on model evaluation see http:// www.oreilly.com/data/free/files/evaluating- machine-learning-models.pdf • For a summary of statistical tests, see: http:// www.pnrjournal.com/viewimage.asp? img=JPharmNegativeResults_2010_1_2_61_7570 8_f1.jpg ✔ Results ✔ Methodology ✔ Abstract & Introduction
22. STEP 4: PRIORITIZE YOUR CHOSEN APPROACHES
23. PRIORIZATION MATRIX High Effort High Impact Quick Wins Major Projects Thankless Tasks Fill-in Jobs
24. STEP 5: PROTOTYPE YOUR CHOSEN APPROACHES
25. A FEW RECOMMENDATIONS Compile a glossary Understand all equations & code Higher level language Reference sections of papers
26. A FEW MORE RECOMMENDATIONS Verify under same conditions Compile the performance in a table
27. MORE RECOMMENDATIONS http://codecapsule.com/2012/01/18/how-to- implement-a-paper/
28. SUMMARY: WHEN SHOULD YOU LOOK FOR RESEARCH PAPERS? • „Somebody must have solved this before!“ • No ready-to-use implementation
29. SUMMARY: A WORKFLOW FOR PROTOTYPING ML PAPERS 1. Search for research findings 2. Decide on your comparison criteria 3. Evaluate quality, relevance and reproducibility 4. Prioritize your chosen approaches 5. Prototype the best approaches
30. HAVE (MORE ) FUN PROTOTYPING! Slides will be tweeted from @ellen_koenig
31. IMAGE CREDITS • Title slide: https://www.flickr.com/photos/ vblibrary/6671465981 • Slide 2: Google calendar & maps • Slide 4: https://www.datasciencecentral.com/ profiles/blogs/140-machine-learning-formulas • Slide 6: https://pixabay.com/de/bremer- stadtmusikanten-skulptur-2444326/
32. IMAGE CREDITS • Slide 8 & 28: pixabay.com • Slide 9: thenounproject.com • Search icon by Luis Prado • Scales icon by Veronica Karenina • Evaluation icon by Dinosoft Labs • Priorities icon by Arthur Shlain • Prototype icon by asianson design
33. IMAGE CREDITS • Slide 10 • https://pixabay.com/en/book-address-book-learning- learn-1171564/ • https://en.wikipedia.org/wiki/Map#/media/ File:World_Map_1689.JPG • Slide 11: thenounproject.com • Network icon by Gregor Cresner • Problem solving icon and razor blade icon by Vector Market • Bank icon by Stock image photo
34. IMAGE CREDITS • Slide 12: https://pxhere.com/en/photo/536212 • Slide 13 • Bar chart icon: pixabay.com • Touch icon by Jasfart for thenounproject.com • Target icon by Libby Ventura for thenounproject.com
35. IMAGE CREDITS • Slide 14: thenounproject.com • Ground breaking icon by faisalovers • Trash icon by UNICORN • Newspaper icon by Aman • Slide 14: Cat icon: pixabay.com • Slide 22: https://pxhere.com/en/photo/109282 • Slide 23: Adapted from: http://www.sixsigmadaily.com/impact-effort-matrix/ • Slide 24: https://pixnio.com/objects/computer/programming-code- programmer-coding-coffee-cup-computer-copy-hands-computer-keyboard
36. IMAGE CREDITS 1. Slide 25: thenounproject.com • Table icon by Yu luck • Pi icon by Sumana Chamrumsorakist • Anaconda icon by parkjisun • Documents icon by Creative Stall 2. Slide 26: thenounproject.com • Maginfiying glass icon by afredocreates.com/icons and flaticons.com • Table icon by Douglas Santos 3. Slide 29: https://commons.wikimedia.org/wiki/ File:Pocketwatch_cutaway_drawing.jpg