pattern: an open source project for migrating predictive models onto apache hadoop

Copyright @2013, Concurrent, Inc.

Paco NathanConcurrent, Inc.San Francisco, CA@pacoid

“Pattern – an open source project for migrating predictive models onto Apache Hadoop”

1Sunday, 17 March 13

https://twitter.com/pacoid


Pattern: predictive models at scale

Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R

• Enterprise Data Workflows• Sample Code• A Little Theory…• Pattern• PMML• Roadmap• Customer Experiments


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.


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


functional programming… in production

• 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


https://github.com/nathanmarz/cascalog/wiki

https://github.com/nathanmarz/cascalog/wiki

https://github.com/twitter/scalding/wiki

https://github.com/twitter/scalding/wiki

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


Hadoop Cluster

sourcetap

sourcetap sink

taptraptap


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


http://conjars.org

http://conjars.org

Hadoop Cluster

sourcetap

sourcetap sink

taptraptap


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


Cascading – deployments

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

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


Cascading – deployments

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


Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R




void map (String doc_id, String text):

for each word w in segment(text):

emit(w, "1");

void reduce (String word, Iterator group):

int count = 0;

for each pc in group:

count += Int(pc);

emit(word, String(count));

The Ubiquitous Word Count

Definition: count how often each word appears in a collection of text documents

This simple program provides an excellent test case for parallel processing, since it illustrates:

• requires a minimal amount of code

• demonstrates use of both symbolic and numeric values

• shows a dependency graph of tuples as an abstraction

• is not many steps away from useful search indexing

• serves as a “Hello World” for Hadoop apps

Any distributed computing framework which can run Word Count efficiently in parallel at scale can handle much larger and more interesting compute problems.

DocumentCollection

WordCount

TokenizeGroupBytoken Count

R

M

count how often each word appears in a collection of text documents


DocumentCollection

WordCount


R

M

1 map 1 reduce18 lines code gist.github.com/3900702

word count – conceptual flow diagram

cascading.org/category/impatient


http://gist.github.com/3900702

http://gist.github.com/3900702

http://www.cascading.org/category/impatient/

http://www.cascading.org/category/impatient/

word count – Cascading app in Java

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();

DocumentCollection

WordCount


R

M


map

reduceEvery('wc')[Count[decl:'count']]

Hfs['TextDelimited[[UNKNOWN]->['token', 'count']]']['output/wc']']

GroupBy('wc')[by:['token']]

Each('token')[RegexSplitGenerator[decl:'token'][args:1]]

Hfs['TextDelimited[['doc_id', 'text']->[ALL]]']['data/rain.txt']']

[head]

[tail]

[{2}:'token', 'count'][{1}:'token']

[{2}:'doc_id', 'text'][{2}:'doc_id', 'text']

wc[{1}:'token'][{1}:'token']

[{2}:'token', 'count'][{2}:'token', 'count']

[{1}:'token'][{1}:'token']

word count – generated flow diagramDocumentCollection

WordCount


R

M


(ns impatient.core (:use [cascalog.api] [cascalog.more-taps :only (hfs-delimited)]) (:require [clojure.string :as s] [cascalog.ops :as c]) (:gen-class))

(defmapcatop split [line] "reads in a line of string and splits it by regex" (s/split line #"[\[\]\\,.)\s]+"))

(defn -main [in out & args] (?<- (hfs-delimited out) [?word ?count] ((hfs-delimited in :skip-header? true) _ ?line) (split ?line :> ?word) (c/count ?count)))

; Paul Lam; github.com/Quantisan/Impatient

word count – Cascalog / ClojureDocumentCollection

WordCount


R

M


github.com/nathanmarz/cascalog/wiki

• implements Datalog in Clojure, with predicates backed by Cascading – for a highly declarative language

• run ad-hoc queries from the Clojure REPL –approx. 10:1 code reduction compared with SQL

• composable subqueries, used for test-driven development (TDD) practices at scale

• Leiningen build: simple, no surprises, in Clojure itself

• more new deployments than other Cascading DSLs – Climate Corp is largest use case: 90% Clojure/Cascalog

• has a learning curve, limited number of Clojure developers

• aggregators are the magic, and those take effort to learn

word count – Cascalog / ClojureDocumentCollection

WordCount


R

M


import com.twitter.scalding._ class WordCount(args : Args) extends Job(args) { Tsv(args("doc"), ('doc_id, 'text), skipHeader = true) .read .flatMap('text -> 'token) { text : String => text.split("[ \\[\\]\$\$,.]") } .groupBy('token) { _.size('count) } .write(Tsv(args("wc"), writeHeader = true))}

word count – Scalding / ScalaDocumentCollection

WordCount


R

M



• extends the Scala collections API so that distributed lists become “pipes” backed by Cascading

• code is compact, easy to understand

• nearly 1:1 between elements of conceptual flow diagram and function calls

• extensive libraries are available for linear algebra, abstract algebra, machine learning – e.g., Matrix API, Algebird, etc.

• significant investments by Twitter, Etsy, eBay, etc.

• great for data services at scale

• less learning curve than Cascalog


WordCount


R

M



• extends the Scala collections API so that distributed lists become “pipes” backed by Cascading

• code is compact, easy to understand

• nearly 1:1 between elements of conceptual flow diagram and function calls

• extensive libraries are available for linear algebra, abstract algebra, machine learning – e.g., Matrix API, Algebird, etc.

• significant investments by Twitter, Etsy, eBay, etc.

• great for data services at scale

• less learning curve than Cascalog


WordCount


R

M

Cascalog and Scalding DSLs leverage the functional aspects of MapReduce, helping limit complexity in process


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


Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R




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


references…

pattern language: a structured method for solving large, complex design problems, where the syntax of the language promotes the use of best practices

amazon.com/dp/0195019199

design patterns: the notion originated in consensus negotiation for architecture, later applied in OOP software engineering by “Gang of Four”

amazon.com/dp/0201633612


http://amazon.com/dp/0195019199




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

design principles of the pattern language ensure best practices for robust, parallel data workflows at scale


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

RHS

M

R


references…

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.”


http://www.literateprogramming.com/

http://www.literateprogramming.com/

Hadoop Cluster

sourcetap

sourcetap sink

taptraptap


Prefs

logslogs

Logs

DataWorkflow

Cache

Customers

Support

WebApp

Reporting

Analytics Cubes

sinktap

Modeling PMML

workflow abstraction – 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.


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


references…

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

Closely related to functional relational programming paradigm:

“Out of the Tar Pit”Moseley & Marks 2006http://goo.gl/SKspn


http://dl.acm.org/citation.cfm?id=362685

http://dl.acm.org/citation.cfm?id=362685

http://goo.gl/SKspn

http://goo.gl/SKspn

workflow abstraction – API design principles

• specify what is required, not how it must be achieved

• plan far ahead, before consuming cluster resources – fail fast prior to submit

• fail the same way twice – deterministic flow planners help reduce engineering costs for debugging at scale

• same JAR, any scale – app does not require a recompile to change data taps or cluster topologies


workflow abstraction – building apps in layers

test-driven development

businessprocess

patternlanguage

flow planner/ optimizer

topology

compiler/build

JVM cluster

separation of concerns: focus on specifying what is required, not how the computers must accomplish it – not unlike BPM/BPEL for BigData

assert expected patterns in tuple flows, adjust assertion levels, verify that tests fail, code until tests pass, repeat … route exceptional data to appropriate department

syntax of the pattern language conveys expertise – much like building a tower with Lego blocks: ensure best practices for robust, parallel data workflows at scale

enables the functional programming aspects: compiler within a compiler, mapping flows to topologies (e.g., create and sequence Hadoop job steps)

entire app is visible to the compiler: resolves issues of crossing boundaries for troubleshooting, exception handling, notifications, etc.; one app = one JAR

cluster scheduler, instrumentation, etc.

Apache Hadoop MR, IMDGs, etc., – upcoming MR2, etc.


workflow abstraction – building apps in layers

test-driven development

businessprocess

patternlanguage

flow planner/ optimizer

topology

compiler/build

JVM cluster

separation of concerns: focus on specifying what is required, not how the computers must accomplish it – not unlike BPM/BPEL for BigData

assert expected patterns in tuple flows, adjust assertion levels, verify that tests fail, code until tests pass, repeat … route exceptional data to appropriate department

syntax of the pattern language conveys expertise – much like building a tower with Lego blocks: ensure best practices for robust, parallel data workflows at scale

enables the functional programming aspects: compiler within a compiler, mapping flows to topologies

entire app is visible to the compiler: resolves issues of crossing boundaries for troubleshooting, exception handling, notifications, etc.; one app = one JAR

cluster scheduler, instrumentation, etc.

Apache Hadoop MR, IMDGs, etc., – upcoming MR2, etc.

several theoretical aspects converge into software engineering practices which minimize the complexity of building and maintaining Enterprise data workflows


Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R




Pattern – analytics workflows

• open source project – ASL 2, GitHub repo

• multiple companies contributing

• complementary to Apache Mahout – while leveraging workflow abstraction, multiple topologies, etc.

• model scoring: generates workflows from PMML models

• model creation: estimation at scale, captured as PMML

• use sample Hadoop app at scale – no coding required

• integrate with 2 lines of Java (1 line Clojure or Scala)

• excellent use cases for customer experiments at scale

cascading.org/pattern


http://www.cascading.org/pattern/


Pattern – analytics workflows

• open source project – ASL 2, GitHub repo

• multiple companies contributing

• complementary to Apache Mahout – while leveraging workflow abstraction, multiple topologies, etc.

• model scoring: generates workflows from PMML models

• model creation: estimation at scale, captured at PMML

• use sample Hadoop app at scale – no coding required

• integrate with 2 lines of Java (1 line Clojure or Scala)

• excellent use cases for customer experiments at scale


greatly reduced development costs, less licensing issues at scale – leveraging the economics of Apache Hadoop clusters, plus the core competencies of analytics staff, plus existing IP in predictive models




Hadoop Cluster

sourcetap

sourcetap sink

taptraptap


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





1. use customer order history as the training data set

2. train a risk classifier for orders, using Random Forest

3. export model from R to PMML

4. build a Cascading app to execute the PMML model

4.1. generate flow from PMML description

4.2. plan the flow for a topology (Hadoop)

4.3. compile app to a JAR file

5. verify results with a regression test

6. deploy the app at scale to calculate scores

7. potentially, reuse classifier for real-time scoring

Pattern – an example classifier

Cascading apps

risk classifierdimension: per-order

risk classifierdimension: customer 360

PMML model

analyst'slaptopdata prep

detectfraudsters

predictmodel costs

customertransactions

score new orders

trainingdata sets

batchworkloads

real-timeworkloads

anomalydetection

segmentcustomers

IMDGHadoop

partner dataDW

ETL

chargebacks,etc.

CustomerDB

velocitymetrics


Cascading apps

risk classifierdimension: per-order

risk classifierdimension: customer 360

PMML model

analyst'slaptopdata prep

detectfraudsters

predictmodel costs

customertransactions

score new orders

trainingdata sets

batchworkloads

real-timeworkloads

anomalydetection

segmentcustomers

IMDGHadoop

partner dataDW

ETL

chargebacks,etc.

CustomerDB

velocitymetrics

Pattern – an example classifier


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

Pattern – create a model in R


<?xml version="1.0"?><PMML version="4.0" xmlns="http://www.dmg.org/PMML-4_0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_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


http://www.dmg.org/PMML-4_0


http://www.w3.org/2001/XMLSchema-instance

http://www.w3.org/2001/XMLSchema-instance



http://www.dmg.org/v4-0/pmml-4-0.xsd

http://www.dmg.org/v4-0/pmml-4-0.xsd

public class Main { public static void main( String[] args ) { String pmmlPath = args[ 0 ]; String ordersPath = args[ 1 ]; String classifyPath = args[ 2 ]; String trapPath = args[ 3 ];

Properties properties = new Properties(); AppProps.setApplicationJarClass( properties, Main.class ); HadoopFlowConnector flowConnector = new HadoopFlowConnector( properties );

// create source and sink taps Tap ordersTap = new Hfs( new TextDelimited( true, "\t" ), ordersPath ); Tap classifyTap = new Hfs( new TextDelimited( true, "\t" ), classifyPath ); Tap trapTap = new Hfs( new TextDelimited( true, "\t" ), trapPath );

// define a "Classifier" model from PMML to evaluate the orders ClassifierFunction classFunc = new ClassifierFunction( new Fields( "score" ), pmmlPath ); Pipe classifyPipe = new Each( new Pipe( "classify" ), classFunc.getInputFields(), classFunc, Fields.ALL );

// connect the taps, pipes, etc., into a flow FlowDef flowDef = FlowDef.flowDef().setName( "classify" ) .addSource( classifyPipe, ordersTap ) .addTrap( classifyPipe, trapTap ) .addSink( classifyPipe, classifyTap );

// write a DOT file and run the flow Flow classifyFlow = flowConnector.connect( flowDef ); classifyFlow.writeDOT( "dot/classify.dot" ); classifyFlow.complete(); }}

Pattern – score a model, within an app


CustomerOrders

Classify ScoredOrders

GroupBytoken

Count

PMMLModel

M R

FailureTraps

Assert

ConfusionMatrix

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


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

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

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

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


bash-3.2$ head out/classify/part-00000 label"var0" var1" var2" order_id" predicted"score1" 0" 1" 0" 6f8e1014" 1" 10" 0" 0" 1" 6f8ea22e" 0" 01" 0" 1" 0" 6f8ea435" 1" 10" 0" 0" 1" 6f8ea5e1" 0" 01" 0" 1" 0" 6f8ea785" 1" 11" 0" 1" 0" 6f8ea91e" 1" 10" 1" 0" 0" 6f8eaaba" 0" 01" 0" 1" 0" 6f8eac54" 1" 10" 1" 1" 0" 6f8eade3" 1" 1

Pattern – evaluating results


# load the JDBC packagelibrary(RJDBC) # set up the driverdrv <- JDBC("cascading.lingual.jdbc.Driver", "~/src/concur/lingual/lingual-local/build/libs/lingual-local-1.0.0-wip-dev-jdbc.jar") # set up a database connection to a local repositoryconnection <- dbConnect(drv, "jdbc:lingual:local;catalog=~/src/concur/lingual/lingual-examples/tables;schema=EMPLOYEES") # query the repository: in this case the MySQL sample database (CSV files)df <- dbGetQuery(connection, "SELECT * FROM EMPLOYEES.EMPLOYEES WHERE FIRST_NAME = 'Gina'")head(df) # use R functions to summarize and visualize part of the datadf$hire_age <- as.integer(as.Date(df$HIRE_DATE) - as.Date(df$BIRTH_DATE)) / 365.25summary(df$hire_age)

library(ggplot2)m <- ggplot(df, aes(x=hire_age))m <- m + ggtitle("Age at hire, people named Gina")m + geom_histogram(binwidth=1, aes(y=..density.., fill=..count..)) + geom_density()

Lingual – connecting Hadoop and R


> summary(df$hire_age) Min. 1st Qu. Median Mean 3rd Qu. Max. 20.86 27.89 31.70 31.61 35.01 43.92

Lingual – connecting Hadoop and R

launchpad.net/test-db

cascading.org/lingual


https://launchpad.net/test-db

https://launchpad.net/test-db

http://www.cascading.org/lingual/

http://www.cascading.org/lingual/

Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R




• 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


http://www.dmg.org/products.html

http://www.dmg.org/products.html

http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language

http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language

• Association Rules: AssociationModel element

• Cluster Models: ClusteringModel element

• Decision Trees: TreeModel element

• Naïve Bayes Classifiers: NaiveBayesModel element

• Neural Networks: NeuralNetwork element

• Regression: RegressionModel and GeneralRegressionModel elements

• Rulesets: RuleSetModel element

• Sequences: SequenceModel element

• Support Vector Machines: SupportVectorMachineModel element

• Text Models: TextModel element

• Time Series: TimeSeriesModel element

PMML – models

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


http://www.ibm.com/developerworks/industry/library/ind-PMML2/

http://www.ibm.com/developerworks/industry/library/ind-PMML2/

PMML – vendor coverage


Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R




roadmap – existing algorithms for scoring

• Random Forest

• Decision Trees

• Linear Regression

• GLM

• Logistic Regression

• K-Means Clustering

• Hierarchical Clustering

• Support Vector Machines





roadmap – top priorities for creating models at scale

• Random Forest

• Logistic Regression

• K-Means Clustering

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





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 @pacoid







Scrubtoken

DocumentCollection

Tokenize

WordCount

GroupBytoken

Count

Stop WordList

Regextoken

HashJoinLeft

RHS

M

R




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


## 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


http://mkseo.pe.kr/stats/?p=220

http://mkseo.pe.kr/stats/?p=220

## 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


http://www.stat.cmu.edu/~cshalizi/490/clustering/clustering01.r

http://www.stat.cmu.edu/~cshalizi/490/clustering/clustering01.r

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


Enterprise Data Workflowswith Cascading

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

references…




blog, dev community, code/wiki/gists, maven repo, commercial products, career opportunities:

cascading.org

zest.to/group11

github.com/Cascading

conjars.org

goo.gl/KQtUL

concurrentinc.com

drill-down…

Copyright @2013, Concurrent, Inc.


http://cascading.org/

http://cascading.org/

http://zest.to/group11

http://zest.to/group11

https://github.com/Cascading

https://github.com/Cascading

http://conjars.org/

http://conjars.org/

http://goo.gl/KQtUL

http://goo.gl/KQtUL

http://concurrentinc.com/

http://concurrentinc.com/

pattern: an open source project for migrating predictive models onto apache hadoop

Technology