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

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"Pattern" is an open source project which takes models trained in popular analytics frameworks, such as SAS, Microstrategy, SQL Server, etc., and runs them at scale on Apache Hadoop. This machine …

"Pattern" is an open source project which takes models trained in popular analytics frameworks, such as SAS, Microstrategy, SQL Server, etc., and runs them at scale on Apache Hadoop. This machine learning library works by translating PMML -- an established XML standard for predictive model markup -- into data workflows based on the Cascading API in Java. PMML models can be run in a pre-defined JAR file with no coding required. PMML can also be combined with other flows based on ANSI SQL (Lingual), Scala (Scalding), Clojure (Cascalog), etc. Multiple companies have collaborated to implement parallelized algorithms: Random Forest, Logistic Regression, K-Means, Hierarchical Clustering, etc., with more machine learning support being added. Benefits include greatly reduced development costs and less licensing issues at scale ?- while leveraging a combination of Apache Hadoop clusters, existing intellectual property in predictive models, and the core competencies of analytics staff. Sample code in the talk will show apps using predictive models built in SAS and R, e.g., anti-fraud classifiers. In addition, examples will show how to compare variations of models for large-scale customer experiments. Portions of this material come from the O`Reilly book "Enterprise Data Workflows with Cascading", due June 2013.

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  • 1. Paco Nathan Concurrent, Inc. San Francisco, CA @pacoid “Pattern – an open source project for migrating predictive models from SAS, etc., onto Hadoop”
  • 2. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 3. Cascading – origins API author 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.
  • 4. Cascading – functional programming 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 • •
  • 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 Modeling PMML 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 • • •
  • 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 Modeling PMML 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 • • •
  • 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 Modeling PMML 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 • • • •
  • 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. • •
  • 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
  • 10. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 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 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
  • 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 Modeling PMML Enterprise Data Workflows Same example… in the back office Organizations have substantial investments in people, infrastructure, process
  • 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 Modeling PMML Enterprise Data Workflows Same example… the heavy lifting! “Main Street” firms are migrating workflows to Hadoop, for cost savings and scale-out
  • 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 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 • • • • • •
  • 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
  • 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 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) • • - - - •
  • 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
  • 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 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. • • • • 1. 2. 3. 4.
  • 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
  • 20. Pattern Language structured method for 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
  • 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
  • 22. Literate Programming by Don Knuth 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.”
  • 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
  • 24. Business Process 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
  • 25. Cascading – functional programming 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 Practices Will Improve Your Return from Technology 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/
  • 26. 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
  • 27. Two Avenues to the 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
  • 28. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 29. 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 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
  • 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 Support Vector Machines: SupportVectorMachineModel element Text Models: TextModel element Time Series: TimeSeriesModel element • • • • • • • • • • • PMML – model coverage ibm.com/developerworks/industry/library/ind-PMML2/
  • 31. PMML – vendor coverage
  • 32. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 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 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 • • • • cascading.org/pattern
  • 34. ## 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
  • 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
  • 36. 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
  • 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
  • 38. ## 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
  • 39. 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 • • • • • • • • • cascading.org/pattern
  • 40. 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 • • • • cascading.org/pattern
  • 41. 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… • • • • cascading.org/pattern
  • 42. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 43. 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 • • •
  • 44. ## 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.error 0 69 16 0.1882353 1 12 103 0.1043478
  • 45. ## 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
  • 46. 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 • • •
  • 47. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 48. Two Cultures “A new research 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: The Two 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)
  • 49. Why Do Ensembles Matter? The World… per Data Modeling The World…
  • 50. 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 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…
  • 51. 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 Diversity Todd Holloway ETech (2008) abeautifulwww.com/EnsembleLearningETech.pdf The Story of the Netflix Prize: An Ensemblers Tale Lester Mackey National Academies Seminar, Washington, DC (2011) stanford.edu/~lmackey/papers/
  • 52. KDD 2013 PMML Workshop 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
  • 53. Failure Traps bonus allocation employee PMML classifier quarterly sales Join Count leads Cascading: background The Workflow Abstraction PMML: Predictive Model Markup Pattern: PMML in Cascading PMML for Customer Experiments Ensemble Models with Pattern Workflow Design Pattern
  • 54. Anatomy of an Enterprise app Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies… ETL data prep predictive model data sources end uses
  • 55. Anatomy of an Enterprise 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
  • 56. Anatomy of an Enterprise 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
  • 57. Anatomy of an Enterprise 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
  • 58. Anatomy of an Enterprise 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…
  • 59. Anatomy of an Enterprise 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…
  • 60. 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 a compiler sees it all… cascading.org
  • 61. a compiler sees it 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
  • 62. a compiler sees it 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 );
  • 63. cascading.org 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 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
  • 64. 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 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
  • 65. Enterprise Data Workflows with Cascading O’Reilly, 2013 amazon.com/dp/1449358721 references… newsletter updates: liber118.com/pxn/
  • 66. 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 • • • • • • • • • acknowledgements…
  • 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…