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Practical Computing with Chaos
Ted Dunning gives a presentation on practical machine learning techniques. He discusses randomized geo-coding to embed spatial data in a total order. He demonstrates Thompson sampling, a Bayesian approach to exploration-exploitation tradeoffs. He also covers dithering in signals and generating synthetic data when real data cannot be shared due to privacy issues.
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Practical Computing with Chaos
- 1. © 2014 MapRTechnologies 1© 2014 MapR Technologies
- 2. © 2014 MapRTechnologies 2 Practical Computing with Chaos Ted Dunning, Chief Applications Architect MapR Technologies Email tdunning@mapr.com tdunning@apache.org Twitter @Ted_Dunning
- 3. © 2014 MapRTechnologies 3 e-book available courtesy of MapR Also at MapR booth http://bit.ly/1jQ9QuL A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman © June 2014 (published by O’Reilly)
- 4. © 2014 MapRTechnologies 4 Practical Machine Learning series (O’Reilly) • Machine learning is becoming mainstream • Need pragmatic approaches that take into account real world business settings: – Time to value – Limited resources – Availability of data – Expertise and cost of team to develop and to maintain system • Look for approaches with big benefits for the effort expended
- 5. © 2014 MapRTechnologies 5 Agenda • Monty Hall • Randomized geo-coding • Thompson sampling – Bayesian Bandits – Targeting – Bayesian ranking • Dithering (sound, signals) • Synthetic data (preview)
- 6. © 2014 MapRTechnologies 6 Let’s Start with Trouble • Monty Hall problem (oops, done) • Three doors, one with a fabulous prize • You pick one • Monte shows you one of the remaining doors is empty • You can switch at this point to the other door or not • Should you switch?
- 7. © 2014 MapRTechnologies 7
- 8. © 2014 MapRTechnologies 8
- 9. © 2014 MapRTechnologies 9
- 10. © 2014 MapRTechnologies 10 The Real Problem • Doing the math isn’t too hard • Convincing somebody you have the right answer is really hard
- 11. © 2014 MapRTechnologies 11 Live Coding With REAL Chaos
- 12. © 2014 MapRTechnologies 12 Geo-coding
- 13. © 2014 MapRTechnologies 13 Geo-coding • Some databases have disk locality key locality • The primary key is totally ordered • Embedding a total ordering of the points in a plane is possible – But loses some distance information – A line is not a square! • We want to do proximity searches – This gets harder in the polar regions for most codings
- 14. © 2014 MapRTechnologies 14 Space Filling Curve 0 1 23 01 2 3 0 1 2 3 0 1 2 3 0 1 2 3
- 15. © 2014 MapRTechnologies 15 Space Filling Curve 0123 2 3 3 1 0 2 2 3 1 1 0 0 3 20 1
- 16. © 2014 MapRTechnologies 16 Z-coding – Interleave Bits 1110 0100 00 1110 11 01 01 10 00 00 11 01 10 01 1100 10
- 17. © 2014 MapRTechnologies 17 Neighbors Often Share Prefix 1110 0100 00 1110 11 01 01 10 00 00 11 01 10 01 1100 10 00. 11.11 10. 01.01 00. 11.01
- 18. © 2014 MapRTechnologies 18 Often, not always Close Far
- 19. © 2014 MapRTechnologies 19 Random Sampling to Derive Keys 1110 0100 00 1110 11 01 01 10 00 00 11 01 10 01 1100 10
- 20. © 2014 MapRTechnologies 20 "00.01.01" "00.01.10" "00.01.11" "00.11.00" "00.11.01" "00.11.10" "00.11.11" "01.00.10" "01.10.00" "01.10.10” 1110 0100 00 1110 11 01 01 10 00 00 11 01 10 01 1100 10
- 21. © 2014 MapRTechnologies 21 "00.01.01" "00.01.10" "00.01.11" "00.11.00" "00.11.01" "00.11.10" "00.11.11" "01.00.10" "01.10.00" "01.10.10” 1110 0100 00 1110 11 01 01 10 00 00 11 01 10 01 1100 10
- 22. © 2014 MapRTechnologies 22 "00.01.10" - "00.01.11" "00.11.00" - "00.11.11" "01.00.10" "01.10.00" - "01.10.10” 1110 0100 00 1110 11 01 01 10 00 00 11 01 10 01 1100 10
- 23. © 2014 MapRTechnologies 23 Dithering
- 24. © 2014 MapRTechnologies 24 • 4 bit sine wave (listen for artifacts as volume decreases) • White dithering (artifacts gone, we hear through the noise) • Noise shaping (noise is easier to hear through)
- 25. © 2014 MapRTechnologies 25 0 1 2 3 4 5 6 −4−2024 Time
- 26. © 2014 MapRTechnologies 26 The Shape of the Noise Noise Frequency −0.4 −0.2 0.0 0.2 0.4 010003000
- 27. © 2014 MapRTechnologies 27 The Effect After Averaging 0 1 2 3 4 5 6 −4−2024 Time
- 28. © 2014 MapRTechnologies 28 Thompson Sampling
- 29. © 2014 MapRTechnologies 29 Learning in the Real World • In the real world we get to pick our training examples – Do we try this restaurant or not? • Learning has real and opportunity costs • Not learning has real and opportunity costs as well • Every sub-optimal choice we make incurs regret – We would like to minimize this – But we can’t quantify regret without incurring regret!
- 30. © 2014 MapRTechnologies 30 An Example • Pick one of five options – Purple, blue, green, red, yellow – Each has a random payoff • If you pick a bad option, regret = mean(best) – mean(yours) • The best known algorithm uses randomization – Best = minimal regret + minimal code complexity
- 31. © 2014 MapRTechnologies 31 Demo – The Algorithm
- 32. © 2014 MapRTechnologies 32 Synthetic Data
- 33. © 2014 MapRTechnologies 33 select IR.ENC_KEY ,IR.ENCOUNTER_ ,IR.ETYPE ,IR.bill_type ,IR.CONTR_ ,IR.SOURCE_CD ,IR.sub_source_cd ,IR.HP_CD ,IR.LOB_CD ,IR.FDO ,IR.TDOS ,IR.member_Nbr ,IR.HIC_NBR ,IR.MEMBER_SOURCE_CD ,IR.HDR_ERRCD ,IR.HDR_ERRDESC ,IR.PROVIDER_NBR ,IR.provider_type ,IR.PROVIDER_SOURCE_CD ,IR.cms_provider_ty e ,IR.SPEC_CD ,IR.SPEC_DESC ,IR.rev_cd ,IR.rev_cd_desc ,IR.proc_cd ,IR.diag_cd ,IR.DIAG_CD_KEY ,IR.DIAGNOSIS_KEY ,IR.rec_state_cd ,IR.rec_status_cd ,IR.DG_ERRCD ,IR.DG_ERRDESC FROM (SELECT distinct enc.encounter_key as ENC_KEY, enc.encounter_nbr as ENCOUNTER_, typ.encounter_type_cd as ETYPE, bt.bill_type, cnt.contract_nbr as CONTR_, ds.SOURCE_CD, enc.sub_source_cd, enc.HP_CD, lob.LOB_CD, enc.new_min_dt as FDOS, substr(enc.new_max_dt, 1, 10) as TDOS, enc.member_Nbr, m.HIC_NBR, m.MEMBER_SOURCE_CD, eerr.error_cd as HDR_ERRCD, eerr.ERROR_DESC as HDR_ERRDESC, enc.PROVIDER_NBR, prv.provider_type, prv.PROVIDER_SOURCE_CD, diag.cms_provider_type, sp.specialty_cd as SPEC_CD, sp.specialty_desc as SPEC_DESC, svc.rev_cd, rev.rev_cd_desc, svc.proc_cd, dgcd.diag_cd, dgcd.DIAG_CD_KEY, diag.DIAGNOSIS_KEY, st.rec_state_cd, sts.rec_status_cd, derr.error_cd as DG_ERRCD, derr.error_desc as DG_ERRDESC FROM oicpcuhg.ir_encounter enc ` Can You See the Problem?
- 34. © 2014 MapRTechnologies 34 INNER JOIN oicpcuhg.ir_encountertype typ ON (typ.encounter_type_key = enc.encounter_type_key) LEFT OUTER JOIN oicpcuhg.ir_billtype bt ON (bt.bill_type_key = enc.bill_type_key) LEFT OUTER JOIN oicpcuhg.ir_contract cnt ON (cnt.contract_key = enc.contract_key) LEFT OUTER JOIN oicpcuhg.ir_datasource ds ON (ds.source_key = enc.data_source_key) LEFT OUTER JOIN oicpcuhg.ir_lineofbusiness lob ON (lob.lob_key = enc.lob_key) INNER JOIN oicpcuhg.ir_member m ON ( m.hp_cd = enc.hp_cd AND m.member_source_cd = enc.member_source_cd AND m.member_nbr = enc.member_nbr) LEFT OUTER JOIN oicpcuhg.ir_encountererror eerror ON (eerror.encounter_key = enc.encounter_key and eerror.active_flg = 'Y') LEFT OUTER JOIN oicpcuhg.ir_error eerr ON (eerr.error_key = eerror.error_key) LEFT OUTER JOIN oicpcuhg.ir_provider prv ON (prv.hp_cd = enc.hp_cd and prv.provider_source_cd = enc.provider_source_cd and prv.provider_nbr = enc.provider_nbr)
- 35. © 2014 MapRTechnologies 35 LEFT OUTER JOIN oicpcuhg.ir_encounterspecialty esp ON (esp.encounter_key = enc.encounter_key) LEFT OUTER JOIN oicpcuhg.ir_specialty sp ON (sp.specialty_key = esp.specialty_key) LEFT OUTER JOIN oicpcuhg.ir_service svc ON (svc.encounter_key = enc.encounter_key) LEFT OUTER JOIN oicpcuhg.ir_revenue rev ON (rev.rev_cd = svc.rev_cd) LEFT OUTER JOIN oicpcuhg.ir_diagnosis diag ON (diag.encounter_key = enc.encounter_key) INNER JOIN oicpcuhg.ir_diagcd dgcd ON (dgcd.diag_cd_key = diag.diag_cd_key) INNER JOIN oicpcuhg.ir_recordstate st ON (st.rec_state_key = diag.rec_state_key) INNER JOIN oicpcuhg.ir_recordstatus sts ON (sts.rec_status_key = diag.rec_status_key) LEFT OUTER JOIN oicpcuhg.ir_diagnosiserror derror ON (derror.diagnosis_key = diag.diagnosis_key and derror.active_flg = 'Y') LEFT OUTER JOIN oicpcuhg.ir_error derr ON (derr.error_key = derror.error_key)) IR INNER JOIN oicpcuhg.umr_req_inbound umr ON (trim(umr.member_nbr) = IR.member_Nbr AND trim(umr.hhc_from_ccyymmdd) = IR.TDOS AND trim(umr.sub_mcare_mbr) = IR.HIC_NBR AND trim(umr.diag1) = IR.diag_cd)
- 36. © 2014 MapRTechnologies 36 One Attack • The customer can’t give you the data – They can’t trust you, by law • But they can probably summarize the data – How many columns – What types – Perhaps statistical summaries
- 37. © 2014 MapRTechnologies 37 Bug Replication Without Security Violation Customer You DataData DataFake DataFake x y α ξ x y α ξ
- 38. © 2014 MapRTechnologies 38 The Upshot • So random numbers are useful • But simple distributions not so much • How can YOU generate cool data?
- 39. © 2014 MapRTechnologies 39 e-book available courtesy of MapR http://bit.ly/1jQ9QuL A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman © June 2014 (published by O’Reilly)
- 40. © 2014 MapRTechnologies 40 Last October: Time Series Databases by Ted Dunning and Ellen Friedman © Oct 2014 (published by O’Reilly)
- 41. © 2014 MapRTechnologies 41 Coming in February: Real World Hadoop by Ted Dunning and Ellen Friedman © Feb 2015 (published by O’Reilly)
- 42. © 2014 MapRTechnologies 42 Thank you for coming today!
Editor's Notes
- #4 Talk track: 2nd in series, first was on how to build a simple recommender. This one on anomaly detection is being sold by O’Reilly on Amazon, but for a limited time MapR is giving away the e-book for free. Here’s the link where you can register to get one.
- #5 Talk track: ELLEN New ways to do it that take into account real world business goals, realistic resources, new types of data and best time to value…