Amidst demo (BNAIC 2015)

1BNAIC 2015 November 5-6, 2015

AMIDST Toolbox A Java library for Analysis of MassIve Data Streams using

Probabilistic Graphical Models

FP7 European research projecthttp://amidst.eu

AndersL.Madsen,AndresR.Masegosa, AnaM.Martinez,Hanen Borchani,ThomasD.Nielsen,Helge Langseth,Antonio

Salmeron, DarioRamos-Lopez.

Outline1. OverviewofAMIDSTToolbox

o Whydatastreamsareimportant?o WhyPGMs foranalyzingdatastreams?o ScalableInference(andlearning)o Roadmapforcomingreleases

2. LiveDemo:Modelingconceptdriftinfinancialdata.o Handlingdatastreams.o DefiningBayesiannetworkswithhidden variables.o InferenceandLearningBayesiannetworks.

BNAIC 2015 November 5-6, 2015

ScopePartI

Data Streams everywhere

• Unboundedflowsofdataaregenerateddaily:• SocialNetworks• NetworkMonitoring• Financial/Bankingindustry• ….


Data Stream Processing

• Processingdatastreamsischallenging:– Donotfitinmainmemory– ContinuousModelupdating– ContinuousModelInference– ConceptDrift


Processing Massive Data Streams

• Everythinghastoscale:• ScalableComputinginfrastructure• ScalableModels/Inference/Learning


AMIDST Toolbox

• Scalableframeworkfordatastreamprocessing.• BasedonProbabilisticGraphicalModels.• UniqueprojectfordatastreamminingusingPGMs.• Opensourceproject(ApacheSoftwareLicense2.0).


AMIDST EU Project

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§ Thistoolboxaimstodealwithreal,complexandmassivedatastreams.§ Appliedtorealuse-casesofAMIDST’sindustrialpartners.


Toolbox Web Page

http://amidst.github.io/toolbox/


WhyPGMsfordatastreamprocessing?

PartII

Why Graphical Models?

§ Let’slookatthefollowingsimpleexample:§ Streamofsensormeasurementsabouttemperature andsmoke presenceinagivengeographicalarea.

§ Monitorthestreamtodetectthepresenceofafire (eventdetectionproblem)

?BNAIC 2015 November 5-6, 2015

§ Casttheproblemasananomalydetectionproblem(outliers).§ StreamingK-Means(widelyusedinindustry).


Anomaly


Why Graphical Models for analyzing Data Streams?§ Manydatastreamsmodelsareblackboxmodels:

§ Pros:§ Noneedtounderstandtheproblem.

§ Cons:§ Manyhyper-parameterstotune.§ Blackbox modelscanrarelyexplainwhattheylearned.

Stream

Blackbox Model

Predictions


§ BayesianNetworks:§ Openboxmodels§ Encodepriorknowledge.§ Continuousanddiscretevariables(CLGnetworks).§ Example:


Fire

Temp Smoke

T1 T2 T3 S1

p(Fire=true|t1,t2,t3,s1)



Stream Predictions

Openbox Models



Stream Predictions

Openbox Models

Blackbox InferenceEngine(multi-coreparallelization)


InferenceEnginePartIII

Inference Engine

§ Queryingthemodel§ p(Fire=true|t1,t2,t3,s1,season)§ E(Temperature|smoke=true).


Inference Engine

§ Queryingthemodel§ p(Fire=true|t1,t2,t3,s1,season)§ E(Temperature|smoke=true)

§ Learningfromdata(usingaBayesianapproach):§ Bayesianframeworknaturallydealswithdatastreams.§ Priorisupdatedinthelightofnewdata.

p(✓|d1, . . . , dn, dn+1) / p(dn+1|✓)p(✓|d1, . . . , dn)


Querying the model

§ ParallelMonteCarloInference[Salmeron etal.CAEPIA2015]

§ ExploitMulti-Core(poweredbyJava8)


Querying the model

§ ParallelMonteCarloInference[Salmeron etal.CAEPIA2015]

§ ExploitMulti-Core(poweredbyJava8)

§ VariationalMessagePassing[Winnetal.JMLR2004]§ Deterministicapproximation


Learning from data streams

§ Bayesianapproach:§ Learningasaninferenceproblem.§ PoweredbyVMP.

✓

Z

x

i = 1 . . . N

↵



§ Bayesianapproach:§ Learningasaninferenceproblem.§ PoweredbyVMP.§ Plateaunotation!!



§ ParallelStreamingVariationalBayes [Brodericketal.NIPS2013]

§ PoweredbyVariationalMessagePassing.§ Multi-coreprocessing(usingJava8).


Links to other open software

§ MoaLink§ MOAisastate-of-the-arttoolfordatastreammining.§ UsingAMIDSTmodelswithinMOAGUI!

§ Greatforevaluation&comparison.


Links to other open software

§ HuginLink§ Hugin isacommercialsoftwareforPGMsandinfluencediagrams.§ Modelsconversion.§ Hugin inferenceenginecanbeusedwithinAMIDST.


RoadMapPartIII

Dynamic Bayesian Networks(release 1.1)

§ Encodetemporalknowledge§ Naturallyfitswithdatastreams

Fire(t)

Temp(t) Smoke(t)

T1(t) T2(t) T3(t) S1(t)

Fire(t-1)

Temp(t-1)


Distributed Stream Processing(release 1.1)

§ RLink§ InvokeAMIDSTInferenceenginewithinR.§ Preliminaryfunctionalityrecentlypresented.


Distributed Stream Processing(release 2.0)

§ FlinkLink§ ApacheFlink:Opensourceplatformfordistributedstreamprocessing.§ HandlingMassiveDataStreams.


Open Source project

§ We’reopentoyourcontributions!!;)


Hosted on Github

§ Download::>git clonehttps://github.com/amidst/toolbox.git

§ Compile::>./compile.sh

§ Run::>./run.sh <class-name>


Please “star” our project!(if you like it)


Any questions before the live demo ?

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LiveDemoTrackingconceptdriftin

FinancialdatawithAMIDST

Borchani etal.ModelingConceptDrift:AProbabilisticGraphicalModelBasedApproach.IDA2015.

Demo Code Available in Github

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eu.amidst.bnaic2015.examples.BCC


Financial Data

§ ProvidedbyBCC(spanish regionalbank).

§ Consistofmonthlyaggregatedinformation§ Activeclientsbetween18and65yearsold.§ DatabetweenApril2007andMarch2014.§ 11variables

§ Income,totalcredit,expenses,etc.

§ Eachclientisclassifiedas:§ defaulter/non-defaulterinfollowing12months.


Financial Data

§ Hypothesis:§ Doesspanish financialcrisisimpactonbankcustomers?§ Lookattheevolutionofregionalunemploymentrate.


Data Preprocessing/Visualization

§ Visualizetheevolutionofthemonthlyaggregateddata:§ Datadoesnotfitinmainmemory!


Model Building

§ WeuseasimpleNaïveBayesmodel:§ Withaglobalhiddenvariabletotrackconceptdrift.

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D

A1 A2 A11…

H


Model Building

§ WealsousePlateaunotation§ “H”isdesignedtocaptureconceptdrift

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D

A1 A2 A11…

HtHt-1

i=1…M

✓


Tracking concept drift


References

§ Masegosaetal.AMIDST:AnalysisofMassiveDataStreamsusingProbabilisticGraphicalModels.Submitted toJMLR.2015.

§ Borchani etal.ModelingConceptDrift:AProbabilisticGraphicalModelBasedApproach.IDA2015.

§ Masegosaetal.Probabilisticgraphicalmodelsonmulti-coreCPUsusingJava8.Submitted toIEEEComputational IntelligenceMagazine,SpecialIssueonComputational IntelligenceSoftware.2015.

§ Salmeron etal.ParallelimportancesamplinginconditionallinearGaussiannetworks.InProceedingsof theConferencia delaAsociacion Españolapara laInteligencia Artificial, volumeinpress,2015.

§ Winnetal. Variationalmessagepassing.JournalofMachineLearningResearch,6:661–694,2005.

§ Brodericketal.Streamingvariational Bayes.InAdvancesinNeuralInformationProcessingSystems,pages1727–1735,2013.


Any questions ?

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http://amidst.github.io/toolbox/BNAIC 2015 November 5-6, 2015

Open Source project

§ We’reopentoyourcontributions!!;)


Amidst demo (BNAIC 2015)

Science

Transcript of Amidst demo (BNAIC 2015)