Treasure Data Summer Internship 2016

Internship final presentation

Blog entry (in Japanese):
http://takuti.me/note/td-intern-2016/

Repository I have created during the internship:
https://github.com/takuti/datadog-anomaly-detector
https://github.com/takuti/norikra-udf-dateformat

Transcript

1. Treasure Data Summer Internship 2016 Real-world Machine Learning
2. $ whoami Takuya Kitazawa github.com/takuti twitter.com/takuti
3. $ curl takuti.me
4. Congrats!
5. Lesson from internship: Machine Learning is difficult…
6. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
7. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
8. Item recommendation = Item ranking problem based on scoring function 1 6 2 3 4 5 How can we evaluate? Which f is better? 1 6 2 3 4 5 items score: 10 8 6 2 1 0.5 user f 1 2 4 recommend
9. Implement 6 ranking measures [B. McFee and G. R. Lanckriet. Metric Learning to Rank. ICML’10]
10. 1. Precision@k Portion of true positives in Y : |X and Y| / |Y| 2. Recall@k Portion of true positives in X : |X and Y| / |X| 3. MAP (Mean Average Precision) Average from Precision@1 to Precision@k truth recommend (use top-k items) X Y
11. 4. AUC (Area Under the ROC Curve) Scores for truth items must be greater than others Portion of “correct” pairs 1 2 4 Expected: 1 1 1 2 2 2 4 4 4 6 3 5 > > > 6 3 5 > > > 6 3 5 > > >
12. 5. MRR (Mean Reciprocal Rank) Rank of first true positive Best: “First true positive is ranked #1” 6. nDCG (normalized Discounted Cumulated Gain) Where is each true positive ranked? Best: 1 6 2 3 4 5 1 2 4 1 2 4 6 3 5 truth others #1 #6 #1 #6
13. … — aggregation SELECT
 precision(t1.rec, t2.truth, 2), recall(t1.rec, t2.truth, 2), average_precision(t1.rec, t2.truth, 2), auc(t1.rec, t2.truth, 2), mrr(t1.rec, t2.truth, 2), ndcg(t1.rec, t2.truth, 2) … — join => 0.500 => 0.333 => 0.333 => 1.000 => 1.000 => 0.613 “higher is better” in [0, 1] range Evaluate top-2 rec. on Hivemall
14. Q. Which one should I use?
15. Q. Which one should I use? A. It depends on your problem
 
 You can try all of them!
16. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
17. Concept behind anomaly detectors Find patterns from past points score “how far from past pattern” Data source: http://cl-www.msi.co.jp/reports/changefinder.html
18. ChangeFinder (CF) by Spring intern Outlier score & Change-point score
19. Implement additional options for CF Parameter estimation logic 1. Solving Yule-Walker equation 2. Burg’s method Scoring function 1. Logloss 2. Hellinger distance new! new!
20. Outlier Change-point Try different combinations (1/2)
21. Outlier Change-point Try different combinations (2/2)
22. CF also has 4 hyperparameters r (float; [0, 1]) k (int) T1 (int) T2 (int) discounting rate order (i.e. complexity) of model window size for outliers window size for change-points
23. Alternative change-point detector: Implement Singular Spectrum Transform (SST)
24. SST is much simpler than CF Naive computationally heavy Efficient numerical approximation easy-to-use, robust method single intuitive hyperparameter: window size w (int) (others can be chosen implicitly)
25. time x 1 182.478 2 176.231 3 183.917 4 177.798 5 165.469 … … SELECT
 time, changefinder(x, “-changepoint_threshold 0.005") FROM
 timeseries ORDER BY time ASC SELECT
 time, sst(x, "-threshold 0.005") FROM
 timeseries ORDER BY time ASC Change-point detection on Hivemall
26. Q. Which method, option and hyperparameter should I choose?
27. Q. Which method, option and hyperparameter should I choose? A. It depends on data and your preference
28. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
29. https://github.com/takuti/datadog-anomaly-detector
30. DD supports (simple) outlier detection Set alert by just thresholding outlier scores We need to detect from more complex conditions 
 reduce false positives (e.g. check if metric-A AND metric-B show high outlier scores) https://www.datadoghq.com/blog/introducing-outlier-detection-in-datadog/
31. Internship Day1: Apply ChangeFinder (Python) for DD metrics Aggregate 1 month points from system.load.norm.5 successfully detected change-point score original points
32. Internship Day2-5: Construct DD anomaly detection system get data points via API new metric for anomaly scores ChangeFinder daemon / CLI tool for replay send record with anomaly scores notify detected anomalies notify errors stream fetch Query
33. Internship Day2-5: Construct DD anomaly detection system get data points via API new metric for anomaly scores ChangeFinder daemon / CLI tool for replay send record with anomaly scores notify detected anomalies notify errors stream fetch Query Yay! My intern has been finished! (?)
34. EPL: Esper’s fancy query language Aggregate metrics (LOOPBACK on Norikra): Detection query: https://github.com/takuti/norikra-udf-dateformat
35. Feedback from @nahi Usability-related requests for daemon’s behavior Supported as soon as possible Feasibility of ChangeFinder for DD metrics CF works as expected on some metrics Hard to figure out useful metrics due to CF’s instability Lack of Norikra-side evaluation
36. Far from conclusion incident? no problem?
37. Q. Which method, option and hyperparameter should I choose? A. It depends on data and your preference Remark:
38. 2-month intern was: enough to implement algorithms & mock system too short to build useful anomaly detector w/ sufficient evaluation Future directions Continuous discussions w/ metric observers More static analysis w/ different methods and options re:dash integration + can be research project
39. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
40. Write tutorial article
41. Churn prediction Focus on subscriber-based services (e.g. mobile telephone network) Churn rate Percentage of individuals who cancelled contract …
42. Step-by-step tutorial on TD w/ td-pandas Preprocessing Model training (80% samples) Prediction (20% samples) Evaluation
43. http://tinyurl.com/td-hivemall-churn-draft
44. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
45. Q. Which method, option and hyperparameter should I choose? A. It depends on data and your preference Customers’ requirements are MOST important
46. Hivemall UDFs
 1. Evaluation of ranking problems
 2. Anomaly detection Datadog anomaly detection — thanks @nahi! Very difficult… Customer churn prediction on TD Random Forest on Hivemall & td-pandas Sales/consulting MTGs Attend 2 MTGs w/ @myui and other members
47. Backbone of real-life machine learning Engineering Wide variety of programming skills Integrating numerous middleware Science Understanding concepts behind equations Having practical point of view (e.g. complexity, usability) Human factor Experience on various data to incorporate heuristics Communication skills to get customers’ requirements
48. Lesson from internship: Machine Learning is difficult…
49. Lesson from internship: Machine Learning is difficult… fun!
50. Treasure Data Summer Internship 2016 Real-world Machine Learning