The presentation given by Chris Severs and myself at the Bay Area Scala Enthusiasts meetup. http://www.meetup.com/Bay-Area-Scala-Enthusiasts/events/105409962/

1. Scalable and Flexible Machine Learning With Scala

2. Who are we?
@BigDataSc @ccsevers
2

3. Stuff you will see today …
 Different types of data scientists – Comparison of different
approaches to develop machine learning flows
 Code
 The five tool tool – Why Scala (and its ecosystem) is the best tool to
develop machine learning flows (Hint: MapReduce is functional)
 Some more code
 Machine Learning examples – Real life (well … almost) examples
of different machine learning problems
 Even more code
3

4. “Good data scientists understand, in a deep
way, that the heavy lifting of cleanup and
preparation is not something that gets in the
way of solving the problem – it is the problem!”
DJ Patil – Founding member of the LinkedIn data science team
4

5. The data funnel
 Real data is an awful, terrible mess
 Cleaning often is a process of operating on data, excluding some
data, bucketing data and calculating aggregates about the data
Generate map, flatMap, for
Exclude filter
Bucket group, groupBy, groupWith
Aggregate sum, reduce, foldLeft
 These blocks form the basis of most data flows
5

6. There are many ways to develop data flows
6

7. The mixer
7

8. {"schema": {
The Mixer Word Count "type": "record",
"name": "WordCount",
#wordcount.py "fields": [
from org.apache.pig.scripting import * {
"name": "word",
@outputSchema("b: bag{ w: chararray}") "type": "string"
def tokenize(words): },
return words.split(" ") {
"name": "count",
script = """ "type": "int"
A = load './input.txt'; }]}}
B = foreach A generate flatten(tokenize((chararray)$0)) as word;
C = group B by word;
D = foreach C generate group, COUNT(B);
store D into './wordcount’ using AvroStorage("schema");
"""
Pig.compile(script).bind().runSingle()
8

9. The Mixer Data Scientist
 Too many occurrences of code inside strings
 Three different languages inside a single file
 User Defined Functions (UDFs) vs. Language Support
 Not real Python, but Jython (which missing some libraries)
 This is just a simple word count!
9

10. The Mixer Data Scientist
 Pig is great at extract, transform, load (ETL)
 … as long as you want to use a function that is already part of the
included library
 … or you get someone else to write it for you (hello, DataFu!)
 Realistically you will need to maintain a Pig code base and a code
base in some language which can run on the JVM
 Pig Latin is a bit funky, missing a lot of core programming language
features
 Pig Latin is interpreted so you get (limited) type and syntax
checking only at runtime
10

11. The Expert
11

12. The Expert Word Count
hadoop fs –get input.txt input.txt
cp /mnt/hadoop/input.txt ~/MyProjects/WordCount/input.txt
##!/usr/bin/perl
use strict;
use warnings;
my %count_of;
while (my $line = <>) { #read from file or STDIN
foreach my $word (split /s+/, $line) {
$count_of{$word}++;
}
}
print "All words and their counts: n";
for my $word (sort keys %count_of) {
print "'$word': $count_of{$word}n";
}
__END__
12

13. The Scalable Expert – Hadoop Streaming
 Lets you use any language you want.
 Same issues as Java MapReduce with regards to multiple passes,
complicated joins, etc.
 Always reading from stdin and writing to stdout.
 Easy to test out on local data
– cat myfile.txt | mymapper.sh | sort | myreducer.sh
 Actual data may not be as nice. No type checking on input or output
can will lead to problems.
 The main reason to do this is so you can use a nice interpreted
language to do your processing.
13

14. The craftsman
14

15. The Craftsman Word Count
package org.myorg; public static class Reduce extends
Reducer<Text, IntWritable, Text, IntWritable> {
import java.io.IOException;
import java.util.*; public void reduce(Text key, Iterable<IntWritable> values, Context
context)
throws IOException, InterruptedException {
import org.apache.hadoop.fs.Path;
int sum = 0;
import org.apache.hadoop.conf.*;
for (IntWritable val : values) {
import org.apache.hadoop.io.*;
sum += val.get();
import org.apache.hadoop.mapreduce.*;
}
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
context.write(key, new IntWritable(sum));
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
}
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
}
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public static void main(String[] args) throws Exception {
public class WordCount {
Configuration conf = new Configuration();
public static class Map extends Mapper<LongWritable, Text, Text,
IntWritable> { Job job = new Job(conf, "wordcount");
private final static IntWritable one = new IntWritable(1);
private Text word = new Text(); job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException { job.setMapperClass(Map.class);
String line = value.toString(); job.setReducerClass(Reduce.class);
StringTokenizer tokenizer = new StringTokenizer(line); job.setInputFormatClass(TextInputFormat.class);
while (tokenizer.hasMoreTokens()) { job.setOutputFormatClass(TextOutputFormat.class);
word.set(tokenizer.nextToken()); FileInputFormat.addInputPath(job, new Path(args[0]));
context.write(word, one); FileOutputFormat.setOutputPath(job, new Path(args[1]));
}
} job.waitForCompletion(true);
} }
15

16. The Craftsman Data Scientist
 If you like Java it works fine
 … until you want to do more than one pass, a complicated join or
anything fancy.
 Cascading solves many of these problems for you but it is still very
verbose
16

17. We need a better tool
A five tool tool!
http://en.wikipedia.org/wiki/Five-tool_player
http://en.wikipedia.org/wiki/Willie_Mays
17

18. The Pragmatic Data Scientist
 Agile – Iterates quickly
 Productive - Uses the right tool for the right job
 Correct - Tests as much as he can before the job is even submitted
 Scalable – Can handle real world problems
 Simple - Single language to represent Operations, UDFs and Data
18

19. The Pragmatic Data Scientist
 Agile – Iterates quickly
 Productive - Uses the right tool for the right job
 Correct - Tests as much as he can before the job is even submitted
 Scalable – Can handle real world problems
 Simple - Single language to represent Operations, UDFs and Data
19

20. Agility – Data is complex
20

21. Agility – Try before you buy
scala> 1 to 10
res0: Range.Inclusive = Range(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
scala> 1 until 10
res1: Range = Range(1, 2, 3, 4, 5, 6, 7, 8, 9)
scala> res0.slice(3, 5)
res3: scala.collection.immutable.IndexedSeq[Int] = Vector(4, 5)
scala> res0.groupBy(_ % 2)
res4: Map[Int, IndexedSeq[Int]] =
Map(1 -> Vector(1, 3, 5, 7, 9), 0 -> Vector(2, 4, 6, 8, 10))
21

22. Productivity – Don't reinvent the wheel
22

23. Productivity – Have the work done for you
Python Collections Operators Scala Collections Operators
 map  foreach  span
 reduce  map  partition
 filter  flatMap  groupBy
 sum  collect  forall
 min/max  find  exists
 takeWhile  count
 dropWhile  fold
 filter  reduce
 withFilter  sum
 filterNot  product
 splitAt  min/max
23

24. Correctness – how to keep your sanity
24

25. Scalability – works on more than your machine
 Integrates with Hadoop (more than just streaming)
 Has the support of scalable libraries
 Parallel by design – not just for M/R flows
25

26. Simplicity
 Paco Nathan, Evil Mad Scientist, Concurrent Inc., @pacoid, says:
– “[Scalding] code is compact, simple to understand”
– “nearly 1:1 between elements of conceptual flow diagram and function
calls”
– “Cascalog and Scalding DSLs leverage the functional aspects of
MapReduce, helping to limit complexity in process”
 Scala is a functional tool for a fundamentally functional job
26

27. Hadoop basics
Let’s count some words
27

28. Let’s count some words
 This is the “Hello, World!” of anything tangentially related to
Hadoop.
 Let’s try it in Scala first without any Hadoop stuff.
 val myLines : Seq[String] = ... // get some stuff
 val myWords = myLines.flatMap(w => w.split("s+"))
 val myWordsGrouped = myWords.groupBy(identity)
 val countedWords = myWordsGrouped.mapValues(x=>x.size)
 Now write out the words somehow
val countedWords = myLines.flatMap(_.split("s+"))
.groupBy(identity)
.mapValues(_.size)
28

29. Let’s count a lot of words
 I’ve gone to the trouble of rewriting this example to run in Hadoop.
 Here it is:
 val myLines : TypedPipe[String] = TextLine(args("input"))
 val myWords = myLines.flatMap(w => w.split("s+"))
 val myWordsGrouped = myWords.groupBy(identity)
 val countedWords = myWordsGrouped.mapValueStream(x =>
Iterator(x.size))
 We can make this even better.
 val countedWords = myWordsGrouped.size
 countedWords.write(TypedTsv[(String,Long)](output))
29

30. Something for nothing
 Other people have already done the hard work to make the
previous example run
 The previous example is using Scalding, a Scala library to write
(mainly) Hadoop MapReduce jobs.
 https://github.com/twitter/scalding
 It even has its own Twitter account, @scalding
 Created by:
– Avi Bryant @avibryant
– Oscar Boykin @posco
– Argyris Zymnis @argyris
 Tweet them now and tell them how awesome it is
 … I’ll wait
30

31. Side by side comparison of local and Hadoop
val myWords = val myWords =
myLines.flatMap(w => myLines.flatMap(w =>
w.split("s+")) w.split("s+"))
val myWordsGrouped = val myWordsGrouped =
myLines.groupBy(identity) myWords.groupBy(identity)
val countedWords = val countedWords =
myWordsGrouped.
myWordsGrouped.
size
mapValues(x=>x.size)
There are some small differences, mainly due to how the
underlying Hadoop process needs to happen.
31

32. Why does this work?
 Scala has support for embedded domain specific languages (DSLs)
 Scalding includes a couple DSLs for specifying Cascading (and by
extension Hadoop) workflows.
 Info about Cascading: http://www.cascading.org/
 One of the Scalding DSLs, the Typed one, is designed to be very
close to the standard Scala collections API
 It’s not a perfect mapping due to how Cascading and Hadoop work,
but in general it is very easy to write your code locally, change a
couple small bits, and have it run on a Hadoop cluster
 Scalding also has a local mode if you want the syntactic sugar
without fussing with Hadoop
32

33. DSLs for everyone!
 We’re showing you Scalding in this talk, but there are others that
are similar.
– Scoobi: https://github.com/NICTA/scoobi
– Scrunch: https://github.com/cloudera/crunch/tree/master/scrunch
 All three attempt to make using code to written on Scala collections
work (almost) seamlessly in Hadoop.
 More on DSLs: http://www.scala-lang.org/node/1403
 Some guts:
33

34. Fields based DSL
From com.twitter.scalding.Dsl
/**
* This object has all the implicit functions and values that are used
* to make the scalding DSL.
*
* It's useful to import Dsl._ when you are writing scalding code outside
* of a Job.
*/
object Dsl extends FieldConversions with TupleConversions with
GeneratedTupleAdders with java.io.Serializable {
implicit def pipeToRichPipe(pipe : Pipe) : RichPipe = new
RichPipe(pipe)
implicit def richPipeToPipe(rp : RichPipe) : Pipe = rp.pipe
}
}
}
34

35. Typed DSL
From com.twitter.scalding.TDsl
/** implicits for the type-safe DSL
* import TDsl._ to get the implicit conversions from
Grouping/CoGrouping to Pipe,
* to get the .toTypedPipe method on standard cascading Pipes.
* to get automatic conversion of Mappable[T] to TypedPipe[T]
*/
object TDsl extends Serializable with GeneratedTupleAdders {
implicit def pipeTExtensions(pipe : Pipe) : PipeTExtensions = new
PipeTExtensions(pipe)
implicit def mappableToTypedPipe[T](mappable : Mappable[T])
(implicit flowDef : FlowDef, mode : Mode, conv :
TupleConverter[T]) : TypedPipe[T] = {
TypedPipe.from(mappable)(flowDef, mode, conv)
}
}
35

36. Algebird – It’s like algebra and a bird
 We did something fancy in the previous example:
 val countedWords = myGroupedWords.size
 val countedWords = myGroupedWords.mapValues(x =>
1L).sum
 val countedWords = myGroupedWords.mapValues(x =>
1L).reduce(implicit mon: Monoid[Long])((l,r) => mon.plus(l,r))
 Scalding uses Algebird extensively to make your life easier.
 Algebird can also be used outside of Scalding with no trouble.
 Algebird has your favorite things like monoids, monads, bloom
filters, count-min sketches, hyperloglogs, etc.
36

37. Counting words with some extra information
 Sometimes we want to know some information about the contexts
that words occurred in. At eBay, this is often the category that a
term appeared in.
 Let’s count words and calculate the entropy of the category
distribution for each word.
– If you’re unfamiliar with this type of entropy just think of it as a
measure of how concentrated the distribution is.
– If you really like formulas it is: Σi p(xi) log(pi)
http://en.wikipedia.org/wiki/Entropy_%28information_theory%29
37

38. More code
case class MyAvroOutput(word: String, count: Long,
entropy: Double) extends AvroRecord
TypedTsv[(String,Int)]
.flatMap{case(line,cat) => line.split("s+").map(x =>
(x,Map(cat->1L))}
.group
.sum
.map{ case(word, dist) =>
val total: Double = dist.values.sum
val entropy = (-1)*dist.values.map{ count =>
(count/total)*math.log(count/total)}.sum
MyAvroOutput(word,total.toLong,entropy)
}
.write(PackedAvroSource[MyAvroOutput](output))
 Math is great
38

39. Machine Learning Examples
The reason why you are here
39

40. Classification case study
How much should we charge for a
Titanic insurance?
40

41. Titanic II case study
 We want to sell life insurance to passengers of Titanic II
 All we have is data from Titanic I
 We have to be able to explain why we charge the prices we do
(damn regulators!)
http://commons.wikimedia.org
41

42. Titanic I Data
 Cabin class – e.g. 1st, 2nd, 3rd ..
 Name – String
 Age – Integer
 Embark place – String
 Destination – String
 Room – Integer
 Ticket – Integer
 Gender – Male or Female
42

43. Titanic Model
http://www.dtreg.com/
43

44. Classifier code
object Titanic {
def main(args: Array[String]) = {
// parse data
val reader = new CSVReader(new FileReader(
"src/main/data/titanic.csv"))
val passengers = reader.readAll.tail.map(Passenger(_))
val instances = passengers.map(_.getInstance).toSet
// build tree
val treeBuilder = new TreeBuilder
val tree = treeBuilder.buildTree(instances)
// print tree
tree.dump(System.out)
}
}
44

45. Titanic Model
http://www.dtreg.com/
45

46. Clustering case study
Let’s cluster some eBay
keywords.
46

47. Motivation
 eBay, like any large site, has a massive number of unique queries
every day
 Identifying groups of queries based on user behavior might help us
to understand the individual queries better
 For queries we are unsure of we can even try and match them into
a cluster that contains queries we know a lot about.
 We can use behavioral things like:
– number of searches
– number of clicks
– number of subsequent bids, buys
– number of exits
– etc
47

48. Let’s use Mahout
 Apache Mahout, http://mahout.apache.org/, @ApacheMahout, is a
powerful machine learning and data mining library that works with
Hadoop.
 It has a ton of great stuff in it, but many of the drawbacks of using
Java MapReduce apply.
 It uses some proprietary data formats (is your data in
VectorWritable SequenceFiles?)
 Luckily for us, there are some nice things that work as standalone
pieces.
 Coming in release 0.8, there is an excellent single pass k-means
clustering algorithm we can use.
48

49. Let’s use Mahout, inside Scalding
lazy val clust = new StreamingKMeans(new FastProjectionSearch(new
EuclideanDistanceMeasure,5,10),
args("sloppyclusters").toInt, (10e-6).asInstanceOf[Float])
var count = 0;
val sloppyClusters =
TextLine(args("input"))
.map{ str =>
val vec = str.split("t").map(_.toDouble)
val cent = new Centroid(count, new DenseVector(vec))
count += 1
cent
}
.toPipe('centroids)
// This won't work with the current build, coming soon though
.unorderedFoldTo[StreamingKMeans,Centroid]('centroids->’clusters)(clust){(cl,cent) =>
cl.cluster(cent); cl}
.toTypedPipe[StreamingKMeans](Dsl.intFields(Seq(0)))
.flatMap(c => c.iterator.asScala.toIterable)
49

50. Let’s use Mahout, inside Scalding
val finalClusters = sloppyClusters.groupAll
.mapValueStream{centList =>
lazy val bclusterer = new BallKMeans(new BruteSearch(
new EuclideanDistanceMeasure),
args("numclusters").toInt, 100)
bclusterer.cluster(centList.toList.asJava)
bclusterer.iterator.asScala
}
.values
50

51. Results
 These are primarily eBay head queries. Remember that the
clustering algorithm knows nothing about the text in the query.
 Sample groups:
– chanel, tory burch, diamond ring, kathy van zeeland handbags, ...
– ipad 4th generation, samsung galaxy s iii, iphone 4 s, nexus 4, ipad
mini, ...
– kohls coupons, lowes coupons
– jcrew, cole haan, diesel, banana republic, gucci, burberry, brooks
brothers, …
– ferrari, utility trailer, polaris ranger, porsche 911, dump truck, bmw
m3, chainsaw, rv, chevelle, vw bus, dodge charger, ...
– paypal account, ebay.com, apple touch icon
precomposed.png, paypal, undefined, ps3%2520games, michael%25
20kors
51

52. Clustering Takeaway
 There are some excellent libraries that exist, and even fit the
functional model
 Scala and Scalding will help you work around the rough edges and
integrate them into your data flow, rather than having to create new
data flows
 Being able to prototype locally and in the Scala REPL saves
massive amounts of developer time
52

53. Matrix API case study
Using LinkedIn endorsement data
to rank Scala experts
53

54. LinkedIn Endorsements
54

55. Page Rank Algorithm
http://commons.wikimedia.org
55

56. Prepare Data
def prepareData = {
// read endorsements and transform to edges
val ends = readFile[Endorsement]("endorsements")
.filter(_.skill == "Scala")
.map(e => (e.sender, e.recipient, 1))
.write(TSV(”edges"))
}
def getDominantEigenVector = { … } // outputs to “ranks” (memberId, rank)
def getMembers = {
// get Bay Area members
val members = readLatest[Member]("members")
.filter(_.getRegionCode == 84)
.groupBy(_.getMemberId.toLong)
// join ranks and members
readFile[Ranks](”ranks”).withReducers(10).join(members).toTypedPipe
.map{ case (id, ((_, rank), m)) =>
(rank, m.getMemberId, m.getFirstName, m.getLastName, m.getHeadline) }
.groupAll.sortBy(_._1).reverse.values
.write(TextLine("talk/scalaRanks"))
} 56

57. Matrix API
 mat.mapValues( func ): Matrix  rowMeanCentering : Matrix
 mat.filterValues( func ) :  rowSizeAveStdev : Matrix
Matrix
 matrix1 * matrix2 : Matrix
 mat.getRow( ind ) :
RowVector  matrix / scalar(Scalar) : Matrix
 mat.reduceRowVectors{ f } :  elemWiseOp( mat2 ){ func }
RowVector  mat1.hProd( matrix2 ) : Matrix
 mat.sumRowVectors :  mat1.zip( mat2/r/c ) : Matrix
RowVector
 matrix.nonZerosWith( sclr )
 mat.mapRows{ func } : Matrix
 matrix.trace : Scalar
 mat.topRowElems( k ) :
Matrix  matrix.sum : Scalar
 mat.rowL1Normalize : Matrix  matrix.transpose : Matrix
 mat.rowL2Normalize : Matrix  mat.diagonal : DiagonalMatrix
57

58. Endorsements Page Rank
Time for Results!
58

59. 59

60. 60

61. Only one slide left!
Summary
61

62. Stuff you have seen today …
 There are many ways to develop machine learning programs, none
of them are perfect
 Scala which reflects the years of evolution since Java's
invention, and Scalding which is the same for vanilla MapReduce,
are a much better alternative
 Machine learning is fun and not necessarily complicated
62

63. 63