Practical Computing with Chaos

© 2014 MapR Technologies 2
Practical Computing with Chaos
Ted Dunning, Chief Applications Architect MapR Technologies
Email tdunning@mapr.com tdunning@apache.org
Twitter @Ted_Dunning

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)

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

Agenda
• Monty Hall
• Randomized geo-coding
• Thompson sampling
– Bayesian Bandits
– Targeting
– Bayesian ranking
• Dithering (sound, signals)
• Synthetic data (preview)

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?

The Real Problem
• Doing the math isn’t too hard
• Convincing somebody you have the right answer is really hard

Live Coding
With REAL Chaos

Geo-coding

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

Space Filling Curve
0 1
23 01
2 3
0
1 2
3 0
1 2
3
0
1 2
3

Space Filling Curve
0123
2
3
3
1
0
2
2
3
1
1
0
0 3
20
1

Z-coding – Interleave Bits
1110
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10
00
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1100
10

Neighbors Often Share Prefix
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1110
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01
10
00
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1100
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00. 11.11
10. 01.01
00. 11.01

Often, not always
Close Far

Random Sampling to Derive Keys
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"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”
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"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
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1100
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Dithering

• 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)

0 1 2 3 4 5 6
−4−2024
Time

The Shape of the Noise
Noise
Frequency
−0.4 −0.2 0.0 0.2 0.4
010003000

The Effect After Averaging
0 1 2 3 4 5 6
−4−2024
Time

Thompson Sampling

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!

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

Demo – The Algorithm

Synthetic Data

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?

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)

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)

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

Bug Replication Without Security Violation
Customer You
DataData
DataFake
DataFake
x y α ξ
x y α ξ

The Upshot
• So random numbers are useful
• But simple distributions not so much
• How can YOU generate cool data?

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)

Thank you for coming today!

Practical Computing with Chaos

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