Post on 29-Jun-2015
description
Paco NathanConcurrent, Inc.San Francisco, CA@pacoid
“Pattern – an open source project for migrating predictive models from SAS, etc., onto Hadoop”
1Tuesday, 25 June 13
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
2Tuesday, 25 June 13
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.
3Tuesday, 25 June 13
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 anyneed to create new languages
• allows programmers who have J2EE expertise to leverage the economics of Hadoop clusters
4Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
5Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
6Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
7Tuesday, 25 June 13
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
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
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
10Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
11Tuesday, 25 June 13
Hadoop Cluster
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taptraptap
customer profile DBsCustomer
Prefs
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DataWorkflow
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Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Enterprise Data Workflows
Same example… in the back office
Organizations have substantial investmentsin people, infrastructure, process
12Tuesday, 25 June 13
Hadoop Cluster
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sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
Modeling PMML
Enterprise Data Workflows
Same example… the heavy lifting!
“Main Street” firms are migratingworkflows to Hadoop, for cost savings and scale-out
13Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
14Tuesday, 25 June 13
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
15Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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)
16Tuesday, 25 June 13
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
17Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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:
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
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.
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
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
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
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
A Pattern LanguageChristopher Alexander, et al.amazon.com/dp/0195019199
20Tuesday, 25 June 13
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
Scrubtoken
DocumentCollection
Tokenize
WordCount
GroupBytoken
Count
Stop WordList
Regextoken
HashJoinLeft
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21Tuesday, 25 June 13
Literate Programming
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 us concentrate rather on explaining to human beings what we want a computer to do.”
22Tuesday, 25 June 13
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
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
24Tuesday, 25 June 13
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/wikigithub.com/twitter/scalding/wiki
Why Adopting the Declarative Programming Practices Will Improve Your Return from TechnologyDan 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
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
26Tuesday, 25 June 13
Two Avenues to the App Layer…
scale ➞co
mpl
exity
➞
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
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
28Tuesday, 25 June 13
• 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
29Tuesday, 25 June 13
• 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/
30Tuesday, 25 June 13
PMML – vendor coverage
31Tuesday, 25 June 13
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
32Tuesday, 25 June 13
Hadoop Cluster
sourcetap
sourcetap sink
taptraptap
customer profile DBsCustomer
Prefs
logslogs
Logs
DataWorkflow
Cache
Customers
Support
WebApp
Reporting
Analytics Cubes
sinktap
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
33Tuesday, 25 June 13
## 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
34Tuesday, 25 June 13
<?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
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
CustomerOrders
Classify ScoredOrders
GroupBytoken
Count
PMMLModel
M R
FailureTraps
Assert
ConfusionMatrix
Pattern – score a model, using pre-defined Cascading app
cascading.org/pattern
37Tuesday, 25 June 13
## 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
38Tuesday, 25 June 13
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
39Tuesday, 25 June 13
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
40Tuesday, 25 June 13
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
41Tuesday, 25 June 13
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
42Tuesday, 25 June 13
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
43Tuesday, 25 June 13
## 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
44Tuesday, 25 June 13
## 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
45Tuesday, 25 June 13
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
46Tuesday, 25 June 13
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
47Tuesday, 25 June 13
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, 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)
48Tuesday, 25 June 13
Why Do Ensembles Matter?
The World…per Data Modeling
The World…
49Tuesday, 25 June 13
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…
50Tuesday, 25 June 13
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/
51Tuesday, 25 June 13
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
52Tuesday, 25 June 13
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
Cascading: backgroundThe Workflow AbstractionPMML: Predictive Model MarkupPattern: PMML in CascadingPMML for Customer ExperimentsEnsemble Models with PatternWorkflow Design Pattern
53Tuesday, 25 June 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
54Tuesday, 25 June 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
ANSI SQL for ETL
55Tuesday, 25 June 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
endusesJ2EE for business logic
56Tuesday, 25 June 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
SAS for predictive models
57Tuesday, 25 June 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
enduses
SAS for predictive modelsANSI SQL for ETL most of the licensing costs…
58Tuesday, 25 June 13
Anatomy of an Enterprise app
Definition a typical Enterprise workflow which crosses through multiple departments, languages, and technologies…
ETL dataprep
predictivemodel
datasources
endusesJ2EE for business logic
most of the project costs…
59Tuesday, 25 June 13
ETL dataprep
predictivemodel
datasources
enduses
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
60Tuesday, 25 June 13
a compiler sees it all…
ETL dataprep
predictivemodel
datasources
enduses
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
a compiler sees it all…
ETL dataprep
predictivemodel
datasources
enduses
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
cascading.orgETL data
preppredictivemodel
datasources
enduses
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
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
63Tuesday, 25 June 13
ETL dataprep
predictivemodel
datasources
enduses
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
FailureTraps
bonusallocation
employee
PMMLclassifier
quarterlysales
Join Countleads
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
Enterprise Data Workflowswith Cascading
O’Reilly, 2013amazon.com/dp/1449358721
references…
newsletter updates:
liber118.com/pxn/
65Tuesday, 25 June 13
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…
66Tuesday, 25 June 13
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