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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 taps
Tap 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 stream
Fields 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 counts
Pipe 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 flow
FlowDef flowDef = FlowDef.flowDef().setName( "wc" )
.addSource( docPipe, docTap )
.addTailSink( wcPipe, wcTap );
// write a DOT file and run the flow
Flow wcFlow = flowConnector.connect( flowDef );
wcFlow.writeDOT( "dot/wc.dot" );
wcFlow.complete();
source and sink taps
for TSV data in HDFS
15Tuesday, 25 June 13](https://image.slidesharecdn.com/hadoopsummit2013-130627004214-phpapp01/85/Hadoop-Summit-Pattern-an-open-source-project-for-migrating-predictive-models-from-SAS-etc-onto-Hadoop-15-320.jpg)
![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 taps
Tap 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 stream
Fields 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 counts
Pipe 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 flow
FlowDef flowDef = FlowDef.flowDef().setName( "wc" )
.addSource( docPipe, docTap )
.addTailSink( wcPipe, wcTap );
// write a DOT file and run the flow
Flow wcFlow = flowConnector.connect( flowDef );
wcFlow.writeDOT( "dot/wc.dot" );
wcFlow.complete();
flow planner for
Apache Hadoop
topology
17Tuesday, 25 June 13](https://image.slidesharecdn.com/hadoopsummit2013-130627004214-phpapp01/85/Hadoop-Summit-Pattern-an-open-source-project-for-migrating-predictive-models-from-SAS-etc-onto-Hadoop-17-320.jpg)
![## 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 <- predicted
confuse <- 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
34Tuesday, 25 June 13](https://image.slidesharecdn.com/hadoopsummit2013-130627004214-phpapp01/85/Hadoop-Summit-Pattern-an-open-source-project-for-migrating-predictive-models-from-SAS-etc-onto-Hadoop-34-320.jpg)
![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
36Tuesday, 25 June 13](https://image.slidesharecdn.com/hadoopsummit2013-130627004214-phpapp01/85/Hadoop-Summit-Pattern-an-open-source-project-for-migrating-predictive-models-from-SAS-etc-onto-Hadoop-36-320.jpg)
Uploaded byPaco Nathan
Hadoop Summit: Pattern – an open source project for migrating predictive models from SAS, etc., onto Hadoop
The document discusses the Cascading framework, a Java API for building large-scale data workflows on Hadoop, emphasizing functional programming principles. It highlights the benefits of using the Predictive Model Markup Language (PMML) for migrating predictive models and integrating various data sources into enterprise data workflows. The document also outlines use cases, integrations, and deployments across different industries that leverage Cascading for efficient data processing.
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Hadoop Summit: Pattern – an open source project for migrating predictive models from SAS, etc., onto Hadoop
- 1. Paco Nathan Concurrent, Inc. SanFrancisco, CA @pacoid “Pattern – an open source project for migrating predictive models from SAS, etc., onto Hadoop” 1Tuesday, 25 June 13
- 2. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 2Tuesday, 25 June 13
- 3. Cascading – origins APIauthor Chris Wensel worked as a system architect at an Enterprise firm well-known for many popular data products. Wensel was following the Nutch open source project – where Hadoop started. Observation: would be difficult to find Java developers to write complex Enterprise apps in MapReduce – potential blocker for leveraging new open source technology. 3Tuesday, 25 June 13
- 4. Cascading – functionalprogramming Key insight: MapReduce is based on functional programming – back to LISP in 1970s. Apache Hadoop use cases are mostly about data pipelines, which are functional in nature. To ease staffing problems as “Main Street” Enterprise firms began to embrace Hadoop, Cascading was introduced in late 2007, as a new Java API to implement functional programming for large-scale data workflows: • leverages JVM and Java-based tools without any need to create new languages • allows programmers who have J2EE expertise to leverage the economics of Hadoop clusters 4Tuesday, 25 June 13
- 5. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML Cascading – definitions • a pattern language for Enterprise Data Workflows • simple to build, easy to test, robust in production • design principles ⟹ ensure best practices at scale 5Tuesday, 25 June 13
- 6. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML Cascading – usage • Java API, DSLs in Scala, Clojure, Jython, JRuby, Groovy,ANSI SQL • ASL 2 license, GitHub src, http://conjars.org • 5+ yrs production use, multiple Enterprise verticals 6Tuesday, 25 June 13
- 7. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML 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 7Tuesday, 25 June 13
- 8. 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. 8Tuesday, 25 June 13
- 9. 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. workflow abstraction addresses: • staffing bottleneck; • system integration; • operational complexity; • test-driven development 9Tuesday, 25 June 13
- 10. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 10Tuesday, 25 June 13
- 11. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML Enterprise Data Workflows Let’s consider a “strawman” architecture for an example app… at the front end LOB use cases drive demand for apps 11Tuesday, 25 June 13
- 12. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML Enterprise Data Workflows Same example… in the back office Organizations have substantial investments in people, infrastructure, process 12Tuesday, 25 June 13
- 13. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML Enterprise Data Workflows Same example… the heavy lifting! “Main Street” firms are migrating workflows to Hadoop, for cost savings and scale-out 13Tuesday, 25 June 13
- 14. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML 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 14Tuesday, 25 June 13
- 15. 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 taps Tap 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 stream Fields 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 counts Pipe 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 flow FlowDef flowDef = FlowDef.flowDef().setName( "wc" ) .addSource( docPipe, docTap ) .addTailSink( wcPipe, wcTap ); // write a DOT file and run the flow Flow wcFlow = flowConnector.connect( flowDef ); wcFlow.writeDOT( "dot/wc.dot" ); wcFlow.complete(); source and sink taps for TSV data in HDFS 15Tuesday, 25 June 13
- 16. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML 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) 16Tuesday, 25 June 13
- 17. 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 taps Tap 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 stream Fields 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 counts Pipe 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 flow FlowDef flowDef = FlowDef.flowDef().setName( "wc" ) .addSource( docPipe, docTap ) .addTailSink( wcPipe, wcTap ); // write a DOT file and run the flow Flow wcFlow = flowConnector.connect( flowDef ); wcFlow.writeDOT( "dot/wc.dot" ); wcFlow.complete(); flow planner for Apache Hadoop topology 17Tuesday, 25 June 13
- 18. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML 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: 1.start from raw inputs in the flow graph 2.define stream assertions for each stage of transforms 3.verify exceptions, code to remove them 4.when impl is complete, app has full test coverage redirect traps in production to Ops, QA, Support,Audit, etc. 18Tuesday, 25 June 13
- 19. 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. Scrub token Document Collection Tokenize Word Count GroupBy token Count Stop Word List Regex token HashJoin Left RHS M R Data is represented as flows of tuples. Operations within the flows bring functional programming aspects into Java In formal terms, this provides a pattern language 19Tuesday, 25 June 13
- 20. Pattern Language structured methodfor solving large, complex design problems, where the syntax of the language ensures the use of best practices – i.e., conveying expertise Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads A Pattern Language Christopher Alexander, et al. amazon.com/dp/0195019199 20Tuesday, 25 June 13
- 21. Workflow Abstraction –literate programming Cascading workflows generate their own visual documentation: flow diagrams in formal terms, flow diagrams leverage a methodology called literate programming provides intuitive, visual representations for apps – great for cross-team collaboration Scrub token Document Collection Tokenize Word Count GroupBy token Count Stop Word List Regex token HashJoin Left RHS M R 21Tuesday, 25 June 13
- 22. Literate Programming by DonKnuth Literate Programming Univ of Chicago Press, 1992 literateprogramming.com/ “Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.” 22Tuesday, 25 June 13
- 23. Workflow Abstraction –business process following the essence of literate programming, Cascading workflows 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 between business 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 then determines how to translate business process into efficient, parallel jobs at scale 23Tuesday, 25 June 13
- 24. Business Process by EdgarCodd “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 24Tuesday, 25 June 13
- 25. 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 mostly based on domain-specific languages (DSLs) in JVM languages which emphasize functional programming: Cascalog in Clojure (2010) Scalding in Scala (2012) github.com/nathanmarz/cascalog/wiki github.com/twitter/scalding/wiki Why Adopting the Declarative Programming PracticesWill ImproveYour Return fromTechnology Dan Woods, 2013-04-17 Forbes forbes.com/sites/danwoods/2013/04/17/why-adopting-the-declarative-programming- practices-will-improve-your-return-from-technology/ 25Tuesday, 25 June 13
- 26. Functional Programming forBig 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 26Tuesday, 25 June 13
- 27. Two Avenues tothe App Layer… scale ➞ complexity➞ Enterprise: must contend with complexity at scale everyday… incumbents extend current practices and infrastructure investments – using J2EE, ANSI SQL, SAS, etc. – to migrate workflows onto Apache Hadoop while leveraging existing staff Start-ups: crave complexity and scale to become viable… new ventures move into Enterprise space to compete using relatively lean staff, while leveraging sophisticated engineering practices, e.g., Cascalog and Scalding 27Tuesday, 25 June 13
- 28. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 28Tuesday, 25 June 13
- 29. • established XMLstandard 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 and supported by over 20 vendors and organizations.With PMML, it is easy to develop a model on one system using one application and deploy the model on another system using another application.” PMML – standard wikipedia.org/wiki/Predictive_Model_Markup_Language 29Tuesday, 25 June 13
- 30. • 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 • SupportVector Machines: SupportVectorMachineModel element • Text Models: TextModel element • Time Series: TimeSeriesModel element PMML – model coverage ibm.com/developerworks/industry/library/ind-PMML2/ 30Tuesday, 25 June 13
- 31. PMML – vendorcoverage 31Tuesday, 25 June 13
- 32. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 32Tuesday, 25 June 13
- 33. Hadoop Cluster source tap source tap sink tap trap tap customer profile DBsCustomer Prefs logs logs Logs Data Workflow Cache Customers Support Web App Reporting Analytics Cubes sink tap ModelingPMML 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 cascading.org/pattern 33Tuesday, 25 June 13
- 34. ## train aRandomForest 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 <- predicted confuse <- 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 34Tuesday, 25 June 13
- 35. <?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_0 http://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 35Tuesday, 25 June 13
- 36. public static voidmain( 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 36Tuesday, 25 June 13
- 37. Customer Orders Classify Scored Orders GroupBy token Count PMML Model M R Failure Traps Assert Confusion Matrix Pattern –score a model, using pre-defined Cascading app cascading.org/pattern 37Tuesday, 25 June 13
- 38. ## run anRF 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 38Tuesday, 25 June 13
- 39. Roadmap – existingalgorithms for scoring • Random Forest • Decision Trees • Linear Regression • GLM • Logistic Regression • K-Means Clustering • Hierarchical Clustering • Multinomial • SupportVector Machines (prepared for release) also, model chaining and general support for ensembles cascading.org/pattern 39Tuesday, 25 June 13
- 40. Roadmap – nextpriorities 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 cascading.org/pattern 40Tuesday, 25 June 13
- 41. Roadmap – toppriorities 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… cascading.org/pattern 41Tuesday, 25 June 13
- 42. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 42Tuesday, 25 June 13
- 43. Experiments – comparingmodels • 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 43Tuesday, 25 June 13
- 44. ## train aRandom 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.error 0 69 16 0.1882353 1 12 103 0.1043478 44Tuesday, 25 June 13
- 45. ## train aLogistic 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 45Tuesday, 25 June 13
- 46. Experiments – comparingresults • 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 46Tuesday, 25 June 13
- 47. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 47Tuesday, 25 June 13
- 48. Two Cultures “A newresearch community using these tools sprang up.Their goal was predictive accuracy.The community consisted of young computer scientists, physicists and engineers plus a few aging statisticians. They began using the new tools in working on complex prediction problems 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: TheTwo Cultures Leo Breiman, 2001 bit.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) 48Tuesday, 25 June 13
- 49. Why Do EnsemblesMatter? The World… per Data Modeling The World… 49Tuesday, 25 June 13
- 50. Algorithmic Modeling “The trickto 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 earlier modeling such as Logistic Regression major learnings from the Netflix Prize: the power of ensembles, model chaining, etc. the problems at hand have become simply too big and too complex for ONE distribution, ONE model, ONE team… 50Tuesday, 25 June 13
- 51. Ensemble Models Breiman:“a multiplicityof 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 PredictionsThrough Diversity Todd Holloway ETech (2008) abeautifulwww.com/EnsembleLearningETech.pdf The Story of the Netflix Prize:An EnsemblersTale Lester Mackey National Academies Seminar,Washington, DC (2011) stanford.edu/~lmackey/papers/ 51Tuesday, 25 June 13
- 52. KDD 2013 PMMLWorkshop Pattern: PMML for Cascading and Hadoop Paco Nathan, Girish Kathalagiri Chicago, 2013-08-11 (accepted) 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining kdd13pmml.wordpress.com 52Tuesday, 25 June 13
- 53. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The WorkflowAbstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern 53Tuesday, 25 June 13
- 54. Anatomy of anEnterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses 54Tuesday, 25 June 13
- 55. Anatomy of anEnterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses ANSI SQL for ETL 55Tuesday, 25 June 13
- 56. Anatomy of anEnterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end usesJ2EE for business logic 56Tuesday, 25 June 13
- 57. Anatomy of anEnterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses SAS for predictive models 57Tuesday, 25 June 13
- 58. Anatomy of anEnterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses SAS for predictive modelsANSI SQL for ETL most of the licensing costs… 58Tuesday, 25 June 13
- 59. Anatomy of anEnterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end usesJ2EE for business logic most of the project costs… 59Tuesday, 25 June 13
- 60. ETL data prep predictive model data sources end uses Lingual: DW → ANSISQL 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. Anatomy of an Enterprise app Cascading allows multiple departments to combine their workflow components into an integrated app – one among many, typically – based on 100% open source a compiler sees it all… cascading.org 60Tuesday, 25 June 13
- 61. a compiler seesit all… ETL data prep predictive model data sources end uses 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. Anatomy of an Enterprise app Cascading allows multiple departments to combine their workflow components into an integrated app – one among many, typically – based on 100% open source 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 61Tuesday, 25 June 13
- 62. a compiler seesit all… ETL data prep predictive model data sources end uses 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. Anatomy of an Enterprise app Cascading allows multiple departments to combine their workflow components into an integrated app – one among many, typically – based on 100% open source FlowDef flowDef = FlowDef.flowDef() .setName( "classifier" ) .addSource( "input", inputTap ) .addSink( "classify", classifyTap ); PMMLPlanner pmmlPlanner = new PMMLPlanner() .setPMMLInput( new File( pmmlModel ) ) .retainOnlyActiveIncomingFields(); flowDef.addAssemblyPlanner( pmmlPlanner ); 62Tuesday, 25 June 13
- 63. cascading.org ETL data prep predictive model data sources end uses Lingual: DW → ANSISQL 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. Anatomy of an Enterprise app Cascading allows multiple departments to combine their workflow components into an integrated app – one among many, typically – based on 100% open source visual collaboration for the business logic is a great way to improve how teams work together Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads 63Tuesday, 25 June 13
- 64. ETL data prep predictive model data sources end uses Lingual: DW → ANSISQL 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. Anatomy of an Enterprise app Cascading allows multiple departments to combine their workflow components into an integrated app – one among many, typically – based on 100% open source Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads 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 64Tuesday, 25 June 13
- 65. Enterprise DataWorkflows with Cascading O’Reilly,2013 amazon.com/dp/1449358721 references… newsletter updates: liber118.com/pxn/ 65Tuesday, 25 June 13
- 66. Many thanks toothers 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 acknowledgements… 66Tuesday, 25 June 13
- 67. 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… 67Tuesday, 25 June 13