pattern - an open source project for migrating predictive models from sas, etc., onto hadoop

Paco NathanConcurrent, Inc.San Francisco, CA@pacoid

“Pattern – an open source project for migrating predictive models from SAS, etc., onto Hadoop”

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Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern

Cascading – origins

API author Chris Wensel worked as a system architectat an Enterprise firm well-known for many popular dataproducts.

Wensel was following the Nutch open source project –where Hadoop started.

Observation: would be difficult to find Java developersto write complex Enterprise apps in MapReduce – potential blocker for leveraging new open sourcetechnology.

Cascading – functional programming

Key insight: MapReduce is based on functional programming– back to LISP in 1970s. Apache Hadoop use cases aremostly about data pipelines, which are functional in nature.

To ease staffing problems as “Main Street” Enterprise firmsbegan to embrace Hadoop, Cascading was introduced in late 2007, as a new Java API to implement functionalprogramming for large-scale data workflows:

leverages JVM and Java-based tools without anyneed to create new languages

allows programmers who have J2EE expertise to leverage the economics of Hadoop clusters

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Cascading – definitions

a pattern language for Enterprise DataWorkflows

simple to build, easy to test, robust inproduction

design principles ⟹ ensure best practices atscale

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Cascading – usage

Java API, DSLs in Scala,Clojure, Jython, JRuby, Groovy, ANSISQL

ASL 2 license, GitHub src, http://conjars.org

5+ yrs production use, multiple Enterprise verticals

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Cascading – integrations

partners: Microsoft Azure, Hortonworks, Amazon AWS, MapR, EMC, SpringSource, Cloudera

taps: Memcached, Cassandra, MongoDB, HBase, JDBC, Parquet, etc.

serialization: Avro, Thrift, Kryo, JSON, etc.

topologies: Apache Hadoop, tuple spaces, local mode

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Cascading – deployments

case studies: Climate Corp, Twitter, Etsy, Williams-Sonoma, uSwitch, Airbnb, Nokia, YieldBot, Square, Harvard, Factual, etc.

use cases: ETL, marketing funnel, anti-fraud, social media, retail pricing, search analytics, recommenders, eCRM, utility grids, telecom, genomics, climatology, agronomics, etc.

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Cascading – deployments

case studies: Climate Corp, Twitter, Etsy, Williams-Sonoma, uSwitch, Airbnb, Nokia, YieldBot, Square, Harvard, Factual, etc.

use cases: ETL, marketing funnel, anti-fraud, social media, retail pricing, search analytics, recommenders, eCRM, utility grids, telecom, genomics, climatology, agronomics, etc.

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workflow abstraction addresses: • staffing bottleneck; • system integration;• operational complexity; • test-driven development

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Modeling PMML

Enterprise Data Workflows

Let’s consider a “strawman” architecture for an example app… at the front end

LOB use cases drive demand for apps

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Enterprise Data Workflows

Same example… in the back office

Organizations have substantial investmentsin people, infrastructure, process

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Enterprise DataWorkflows

Same example… the heavy lifting!

“Main Street” firms are migratingworkflows to Hadoop, for cost savings and scale-out

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Modeling PMML

Cascading workflows – taps

taps integrate other data frameworks, as tuple streams

these are “plumbing” endpoints in the pattern language

sources (inputs), sinks (outputs), traps (exceptions)

text delimited, JDBC, Memcached, HBase, Cassandra, MongoDB, etc.

data serialization: Avro, Thrift, Kryo, JSON, etc.

extend a new kind of tap in just a few lines of Java

schema and provenance get derived from analysis of the taps

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Cascading workflows – taps

String docPath = args[ 0 ];String wcPath = args[ 1 ];Properties properties = new Properties();AppProps.setApplicationJarClass( properties, Main.class );HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties );

// create source and sink tapsTap docTap = new Hfs( new TextDelimited( true, "\t" ), docPath );Tap wcTap = new Hfs( new TextDelimited( true, "\t" ), wcPath );

// specify a regex to split "document" text lines into token streamFields token = new Fields( "token" );Fields text = new Fields( "text" );RegexSplitGenerator splitter = new RegexSplitGenerator( token, "[ \\[\\]\$\$,.]" );// only returns "token"Pipe docPipe = new Each( "token", text, splitter, Fields.RESULTS );// determine the word countsPipe wcPipe = new Pipe( "wc", docPipe );wcPipe = new GroupBy( wcPipe, token );wcPipe = new Every( wcPipe, Fields.ALL, new Count(), Fields.ALL );

// connect the taps, pipes, etc., into a flowFlowDef flowDef = FlowDef.flowDef().setName( "wc" ).addSource( docPipe, docTap ) .addTailSink( wcPipe, wcTap );// write a DOT file and run the flowFlow wcFlow = flowConnector.connect( flowDef );wcFlow.writeDOT( "dot/wc.dot" );wcFlow.complete();

source and sink tapsfor TSV data in HDFS

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Modeling PMML

Cascading workflows – topologies

topologies execute workflows on clusters

flow planner is like a compiler for queries

Hadoop (MapReduce jobs)

local mode (dev/test or special config)

in-memory data grids (real-time)

flow planner can be extended to support other topologies

blend flows in different topologies into the same app – for example,batch (Hadoop) + transactions (IMDG)

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Cascading workflows – topologies

String docPath = args[ 0 ];String wcPath = args[ 1 ];Properties properties = new Properties();AppProps.setApplicationJarClass( properties, Main.class );HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties );

// create source and sink tapsTap docTap = new Hfs( new TextDelimited( true, "\t" ), docPath );Tap wcTap = new Hfs( new TextDelimited( true, "\t" ), wcPath );

// specify a regex to split "document" text lines into token streamFields token = new Fields( "token" );Fields text = new Fields( "text" );RegexSplitGenerator splitter = new RegexSplitGenerator( token, "[ \\[\\]\$\$,.]" );// only returns "token"Pipe docPipe = new Each( "token", text, splitter, Fields.RESULTS );// determine the word countsPipe wcPipe = new Pipe( "wc", docPipe );wcPipe = new GroupBy( wcPipe, token );wcPipe = new Every( wcPipe, Fields.ALL, new Count(), Fields.ALL );

// connect the taps, pipes, etc., into a flowFlowDef flowDef = FlowDef.flowDef().setName( "wc" ).addSource( docPipe, docTap ) .addTailSink( wcPipe, wcTap );// write a DOT file and run the flowFlow wcFlow = flowConnector.connect( flowDef );wcFlow.writeDOT( "dot/wc.dot" );wcFlow.complete();

flow planner for Apache Hadoop topology

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Modeling PMML

Cascading workflows – test-driven development

assert patterns (regex) on the tuple streams

adjust assert levels, like log4j levels

trap edge cases as “data exceptions”

TDD at scale:

start from raw inputs in the flow graph

define stream assertions for each stage of transforms

verify exceptions, code to remove them

when impl is complete, app has full test coverage

redirect traps in production to Ops, QA, Support, Audit, etc.

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Workflow Abstraction – pattern language

Cascading uses a “plumbing” metaphor in the Java API, to define workflows out of familiar elements: Pipes, Taps,Tuple Flows, Filters, Joins, Traps, etc.

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Data is represented as flows of tuples. Operations withinthe flows bring functional programming aspects into Java

In formal terms, this provides a pattern language

Pattern Language

structured method for solving large, complex designproblems, where the syntax of the language ensures the use of best practices – i.e., conveying expertise

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A Pattern LanguageChristopher Alexander, et al.amazon.com/dp/0195019199

Workflow Abstraction – literate programming

Cascading workflows generate their own visualdocumentation: flow diagrams

in formal terms, flow diagrams leverage a methodologycalled literate programming

provides intuitive, visual representations for apps –great for cross-team collaboration

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LiterateProgramming

by Don Knuth

Literate ProgrammingUniv of Chicago Press, 1992

literateprogramming.com/

“Instead of imagining that our main task is to instruct a computer what to do, let usconcentrate rather on explaining to humanbeings what we want a computer to do.”

Workflow Abstraction – business process

following the essence of literate programming, Cascadingworkflows provide statements of business process

this recalls a sense of business process management for Enterprise apps (think BPM/BPEL for Big Data)

Cascading creates a separation of concerns betweenbusiness process and implementation details (Hadoop, etc.)

this is especially apparent in large-scale Cascalog apps:

“Specify what you require, not how to achieve it.”

by virtue of the pattern language, the flow planner thendetermines how to translate business process into efficient,parallel jobs at scale

BusinessProcess

by Edgar Codd

“A relational model of data for large shared data banks”Communications of the ACM, 1970 dl.acm.org/citation.cfm?id=362685

rather than arguing between SQL vs. NoSQL…structured vs. unstructured data frameworks… this approach focuses on what apps do:

the process of structuring data

Cascading – functionalprogramming

Twitter, eBay, LinkedIn, Nokia, YieldBot, uSwitch, etc., have invested in open source projects atop Cascading– used for their large-scale production deployments

new case studies for Cascading apps are mostlybased on domain-specific languages (DSLs) in JVMlanguages which emphasize functional programming:

Cascalog in Clojure (2010)Scalding in Scala (2012)

github.com/nathanmarz/cascalog/wikigithub.com/twitter/scalding/wiki

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Why Adopting the Declarative Programming Practices Will Improve Your Return fromTechnologyDan Woods, 2013-04-17 Forbes

forbes.com/sites/danwoods/2013/04/17/why-adopting-the-declarative-programming-practices-will-improve-your-return-from-technology/

Functional Programming for Big Data

WordCount with token scrubbing…

Apache Hive: 52 lines HQL + 8 lines Python (UDF)

compared to

Scalding: 18 lines Scala/Cascading

functional programming languages help reduce software engineering costs at scale, over time

Two Avenues to the App Layer…

scale ➞co

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Enterprise: must contend withcomplexity at scale everyday…

incumbents extend current practices andinfrastructure investments – using J2EE,ANSI SQL, SAS, etc. – to migrateworkflows onto Apache Hadoop whileleveraging existing staff

Start-ups: crave complexity and scale to become viable…

new ventures move into Enterprise spaceto compete using relatively lean staff, while leveraging sophisticated engineeringpractices, e.g., Cascalog and Scalding

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established XML standard for predictive model markup

organized by Data Mining Group (DMG), since 1997 http://dmg.org/

members: IBM, SAS, Visa, NASA, Equifax, Microstrategy, Microsoft, etc.

PMML concepts for metadata, ensembles, etc., translate directly into Cascading tuple flows

“PMML is the leading standard for statistical and data mining models andsupported by over 20 vendors and organizations. With PMML, it is easy to develop a model on one system using one application and deploy themodel on another system using another application.”

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PMML – standard

wikipedia.org/wiki/Predictive_Model_Markup_Language

Association Rules: AssociationModel element

Cluster Models: ClusteringModel element

Decision Trees: TreeModel element

Naïve Bayes Classifiers: NaiveBayesModel element

Neural Networks: NeuralNetwork element

Regression: RegressionModel and GeneralRegressionModel elements

Rulesets: RuleSetModel element

Sequences: SequenceModel element

Support Vector Machines: SupportVectorMachineModel element

Text Models: TextModel element

Time Series: TimeSeriesModel element

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PMML – model coverage

ibm.com/developerworks/industry/library/ind-PMML2/

PMML – vendor coverage

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Modeling PMML

Pattern – model scoring

migrate workloads: SAS,Teradata, etc., exporting predictive models as PMML

great open source tools – R, Weka, KNIME, Matlab, RapidMiner, etc.

integrate with other libraries –Matrix API, etc.

leverage PMML as another kind of DSL

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cascading.org/pattern

## train a RandomForest model f <- as.formula("as.factor(label) ~ .")fit <- randomForest(f, data_train, ntree=50) ## test the model on the holdout test set print(fit$importance)print(fit) predicted <- predict(fit, data)data$predicted <- predictedconfuse <- table(pred = predicted, true = data[,1])print(confuse) ## export predicted labels to TSV write.table(data, file=paste(dat_folder, "sample.tsv", sep="/"), quote=FALSE, sep="\t", row.names=FALSE) ## export RF model to PMML saveXML(pmml(fit), file=paste(dat_folder, "sample.rf.xml", sep="/"))

Pattern – create a model in R

<?xml version="1.0"?>

<PMML version="4.0" xmlns="http://www.dmg.org/PMML-4_0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_0http://www.dmg.org/v4-0/pmml-4-0.xsd"> <Header copyright="Copyright (c)2012 Concurrent, Inc." description="Random Forest Tree Model"> <Extension name="user" value="ceteri" extender="Rattle/PMML"/> <Application name="Rattle/PMML" version="1.2.30"/> <Timestamp>2012-10-22 19:39:28</Timestamp> </Header> <DataDictionary numberOfFields="4"> <DataField name="label" optype="categorical" dataType="string"> <Value value="0"/> <Value value="1"/> </DataField> <DataField name="var0" optype="continuous" dataType="double"/> <DataField name="var1" optype="continuous" dataType="double"/> <DataField name="var2" optype="continuous" dataType="double"/> </DataDictionary> <MiningModel modelName="randomForest_Model" functionName="classification"> <MiningSchema> <MiningField name="label" usageType="predicted"/> <MiningField name="var0" usageType="active"/> <MiningField name="var1" usageType="active"/> <MiningField name="var2" usageType="active"/> </MiningSchema> <Segmentation multipleModelMethod="majorityVote"> <Segment id="1"> <True/> <TreeModel modelName="randomForest_Model" functionName="classification" algorithmName="randomForest" splitCharacteristic="binarySplit"> <MiningSchema> <MiningField name="label" usageType="predicted"/> <MiningField name="var0" usageType="active"/> <MiningField name="var1" usageType="active"/> <MiningField name="var2" usageType="active"/> </MiningSchema>...

Pattern – capture model parameters as PMML

public static void main( String[] args ) throws RuntimeException { String inputPath = args[ 0 ]; String classifyPath = args[ 1 ]; // set up the config properties Properties properties = new Properties(); AppProps.setApplicationJarClass( properties, Main.class ); HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties ); // create source and sink taps Tap inputTap = new Hfs( new TextDelimited( true, "\t" ), inputPath ); Tap classifyTap = new Hfs( new TextDelimited( true, "\t" ), classifyPath ); // handle command line options OptionParser optParser = new OptionParser(); optParser.accepts( "pmml" ).withRequiredArg(); OptionSet options = optParser.parse( args ); // connect the taps, pipes, etc., into a flow FlowDef flowDef = FlowDef.flowDef().setName( "classify" ) .addSource( "input", inputTap ) .addSink( "classify", classifyTap ); if( options.hasArgument( "pmml" ) ) { String pmmlPath = (String) options.valuesOf( "pmml" ).get( 0 ); PMMLPlanner pmmlPlanner = new PMMLPlanner() .setPMMLInput( new File( pmmlPath ) ) .retainOnlyActiveIncomingFields() .setDefaultPredictedField( new Fields( "predict", Double.class ) ); // default value if missing from the model flowDef.addAssemblyPlanner( pmmlPlanner ); } // write a DOT file and run the flow Flow classifyFlow = flowConnector.connect( flowDef ); classifyFlow.writeDOT( "dot/classify.dot" ); classifyFlow.complete(); }

Pattern – score a model, within an app

CustomerOrders

Classify ScoredOrders

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Pattern – score a model, using pre-defined Cascading app


## run an RF classifier at scale hadoop jar build/libs/pattern.jar data/sample.tsv out/classify out/trap \--pmml data/sample.rf.xml

## run an RF classifier at scale, assert regression test, measure confusion matrix hadoop jar build/libs/pattern.jar data/sample.tsv out/classify out/trap \--pmml data/sample.rf.xml --assert --measure out/measure

## run a predictive model at scale, measure RMSE hadoop jar build/libs/pattern.jar data/iris.lm_p.tsv out/classify out/trap \ --pmml data/iris.lm_p.xml --rmse out/measure

Pattern – score a model, using pre-defined Cascading app

Roadmap – existing algorithms for scoring

Random Forest

Decision Trees

Linear Regression

GLM

Logistic Regression

K-Means Clustering

Hierarchical Clustering

Multinomial

Support Vector Machines (prepared for release)

also, model chaining and general support for ensembles

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Roadmap – next priorities for scoring

Time Series (ARIMA forecast)

Association Rules (basket analysis)

Naïve Bayes

Neural Networks

algorithms extended based on customer use cases – contact groups.google.com/forum/?fromgroups#!forum/pattern-user

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Roadmap – top priorities for creating models at scale

Random Forest

Logistic Regression

K-Means Clustering

Association Rules

…plus all models which can be trained via sparse matrix factorization(TQSR => PCA, SVD least squares, etc.)

a wealth of recent research indicates many opportunities to parallelize popular algorithms for training models at scale on Apache Hadoop…

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Experiments – comparing models

much customer interest in leveraging Cascading and Apache Hadoop to run customer experiments at scale

run multiple variants, then measure relative “lift”

Concurrent runtime – tag and track models

the following example compares two models trained with different machine learning algorithms

this is exaggerated, one has an important variable intentionally omitted to help illustrate the experiment

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## train a Random Forest model## example: http://mkseo.pe.kr/stats/?p=220 f <- as.formula("as.factor(label) ~ var0 + var1 + var2")fit <- randomForest(f, data=data, proximity=TRUE, ntree=25)print(fit)saveXML(pmml(fit), file=paste(out_folder, "sample.rf.xml", sep="/"))

Experiments – Random Forest model

OOB estimate of error rate: 14%Confusion matrix: 0 1 class.error0 69 16 0.18823531 12 103 0.1043478

## train a Logistic Regression model (special case of GLM)## example: http://www.stat.cmu.edu/~cshalizi/490/clustering/clustering01.r f <- as.formula("as.factor(label) ~ var0 + var2")fit <- glm(f, family=binomial, data=data)print(summary(fit))saveXML(pmml(fit), file=paste(out_folder, "sample.lr.xml", sep="/"))

Experiments – Logistic Regression model

Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.8524 0.3803 4.871 1.11e-06 ***var0 -1.3755 0.4355 -3.159 0.00159 ** var2 -3.7742 0.5794 -6.514 7.30e-11 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

NB: this model has “var1” intentionally omitted

Experiments – comparing results

use a confusion matrix to compare results for the classifiers

Logistic Regression has a lower “false negative” rate (5% vs. 11%)however it has a much higher “false positive” rate (52% vs. 14%)

assign a cost model to select a winner –for example, in an ecommerce anti-fraud classifier:

FN ∼ chargeback risk FP ∼ customer support costs

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Two Cultures

“A new research community using these tools sprang up. Their goalwas predictive accuracy. The community consisted of young computerscientists, physicists and engineers plus a few aging statisticians. They began using the new tools in working on complex predictionproblems where it was obvious that data models were not applicable:speech recognition, image recognition, nonlinear time series prediction,handwriting recognition, prediction in financial markets.”

Statistical Modeling: The Two Cultures Leo Breiman, 2001bit.ly/eUTh9L

in other words, seeing the forest for the trees…

this paper chronicled a sea change from data modeling practices(silos, manual process) to the rising use of algorithmic modeling(machine data for automation/optimization)

Why Do Ensembles Matter?

The World…per Data Modeling

The World…

Algorithmic Modeling

“The trick to being a scientist is to be open to using a wide variety of tools.” – Breiman

circa 2001: Random Forest, bootstrap aggregation, etc., yield dramatic increases in predictive power over earliermodeling such as Logistic Regression

major learnings from the Netflix Prize: the power ofensembles, model chaining, etc.

the problems at hand have become simply too big and toocomplex for ONE distribution, ONE model, ONE team…

Ensemble Models

Breiman: “a multiplicity of data models”

BellKor team: 100+ individual models in 2007 Progress Prize

while the process of combining models adds complexity(making it more difficult to anticipate or explain predictions)accuracy may increase substantially

Ensemble Learning: Better Predictions Through DiversityTodd HollowayETech (2008)abeautifulwww.com/EnsembleLearningETech.pdf

The Story of the Netflix Prize: An Ensemblers TaleLester MackeyNational Academies Seminar, Washington, DC (2011)stanford.edu/~lmackey/papers/

KDD 2013 PMML Workshop

Pattern: PMML for Cascading and HadoopPaco Nathan, Girish KathalagiriChicago, 2013-08-11 (accepted)

19th ACM SIGKDD Conference on Knowledge Discovery and Data Miningkdd13pmml.wordpress.com

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Anatomy of an Enterprise app

Definition a typical Enterprise workflow which crosses throughmultiple departments, languages, and technologies…

ETL dataprep

predictivemodel

datasources

enduses



ETL dataprep

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ANSI SQL for ETL



ETL dataprep

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endusesJ2EE for business logic



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SAS for predictive models



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SAS for predictive modelsANSI SQL for ETL most of the licensing costs…



ETL dataprep

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most of the project costs…

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Lingual:DW → ANSI SQL

Pattern:SAS, R, etc. → PMML

business logic in Java, Clojure, Scala, etc.

sink taps for Memcached, HBase, MongoDB, etc.

source taps for Cassandra, JDBC,Splunk, etc.


Cascading allows multiple departments to combine their workflow componentsinto an integrated app – one among many, typically – based on 100% open source

a compiler sees it all…

cascading.org


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FlowDef flowDef = FlowDef.flowDef() .setName( "etl" ) .addSource( "example.employee", emplTap ) .addSource( "example.sales", salesTap ) .addSink( "results", resultsTap ); SQLPlanner sqlPlanner = new SQLPlanner() .setSql( sqlStatement ); flowDef.addAssemblyPlanner( sqlPlanner );

cascading.org


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FlowDef flowDef = FlowDef.flowDef() .setName( "classifier" ) .addSource( "input", inputTap ) .addSink( "classify", classifyTap ); PMMLPlanner pmmlPlanner = new PMMLPlanner() .setPMMLInput( new File( pmmlModel ) ) .retainOnlyActiveIncomingFields(); flowDef.addAssemblyPlanner( pmmlPlanner );

cascading.orgETL data

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visual collaboration for the business logic is a great wayto improve how teams work together

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multiple departments, working in their respective

frameworks, integrate results into a combined app,

which runs at scale on a cluster… business process

combined in a common space (DAG) for flow

planners, compiler, optimization, troubleshooting,

exception handling, notifications, security audit,

performance monitoring, etc.

cascading.org

Enterprise Data Workflowswith Cascading

O’Reilly, 2013amazon.com/dp/1449358721

references…

newsletter updates:

liber118.com/pxn/

Many thanks to others who have contributed code,ideas, suggestions, etc., to Pattern:

Chris Wensel @ Concurrent

Girish Kathalagiri @ AgilOne

Vijay Srinivas Agneeswaran @ Impetus

Chris Severs @ eBay

Ofer Mendelevitch @ Hortonworks

Sergey Boldyrev @ Nokia

Quinton Anderson @ IZAZI Solutions

Chris Gutierrez @ Airbnb

Villu Ruusmann @ JPMML project

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acknowledgements…

blog, developer community, code/wiki/gists, maven repo, commercial products, etc.:

cascading.org

zest.to/group11

github.com/Cascading

conjars.org

goo.gl/KQtUL

concurrentinc.com

drill-down…

pattern - an open source project for migrating predictive models from sas, etc., onto hadoop

Technology

cascading pmml

workflow abstraction

data frameworks

data pipelines

largescale data workflows

apache hadoop use cases

new open source technology

nutch open source project