Building Machine Learning Applications with Sparkling Water: Writing applications which are processing and analyzing large amount of data is still hard. It often requires to design and run Machine Learning experiments in small scale and then consolidate them into a form of application and run them in large scale. There are several distributed machine learning platforms which are trying to mitigate this effort. In this talk we will focus on Sparkling Water which is combining benefits of two platforms – H2O and Spark. H2O is an open-source distributed math-engine providing tuned Machine Learning library, Spark is an execution platform which allows for processing large amount of data. The talk will demonstrate Sparkling Water features and shows its benefits for building rich and robust Machine Learning applications.

1. Building Machine
Learning Applications
with Sparkling Water
MLConf 2015 NYC
Michal Malohlava and Alex Tellez and H2O.ai

2. TBD
Head of Sales
Distributed
Systems
Engineers
Making
ML Scale!
Team@H2O.ai

3. Scalable
Machine Learning
For Smarter
Applications

4. Smarter Applications

5. Scalable Applications
Distributed
Able to process huge amount of data from
different sources
Easy to develop and experiment
Powerful machine learning engine inside

6. BUT
how to build
them?

7. Build an application
with …

8. Build an application
with …
?

9. …with Spark and H2O

10. Open-source distributed execution platform
User-friendly API for data transformation based on RDD
Platform components - SQL, MLLib, text mining
Multitenancy
Large and active community

11. Open-source scalable machine
learning platform
Tuned for efficient computation
and memory use
Production ready machine
learning algorithms
R, Python, Java, Scala APIs
Interactive UI, robust data parser

13. Sparkling Water
Provides
Transparent integration of H2O with Spark ecosystem
Transparent use of H2O data structures and
algorithms with Spark API
Platform for building Smarter Applications
Excels in existing Spark workflows requiring
advanced Machine Learning algorithms

14. Lets build
an application !

16. OR

17. OR

18. OR
Detect spam text messages

19. Data example

20. ML Workflow
1. Extract data
2. Transform, tokenize messages
3. Build Tf-IDF
4. Create and evaluate
Deep Learning model
5. Use the model
Goal: For a given text message identify if
it is spam or not

21. Lego #1: Data load
// Data load
def load(dataFile: String): RDD[Array[String]] = {
sc.textFile(dataFile).map(l => l.split(“t"))
.filter(r => !r(0).isEmpty)
}

22. Lego #2: Ad-hoc
Tokenization
def tokenize(data: RDD[String]): RDD[Seq[String]] = {
val ignoredWords = Seq("the", “a", …)
val ignoredChars = Seq(',', ‘:’, …)
val texts = data.map( r => {
var smsText = r.toLowerCase
for( c <- ignoredChars) {
smsText = smsText.replace(c, ' ')
}
val words =smsText.split(" ").filter(w =>
!ignoredWords.contains(w) && w.length>2).distinct
words.toSeq
})
texts
}

23. Lego #3: Tf-IDF
def buildIDFModel(tokens: RDD[Seq[String]],
minDocFreq:Int = 4,
hashSpaceSize:Int = 1 << 10):
(HashingTF, IDFModel, RDD[Vector]) = {
// Hash strings into the given space
val hashingTF = new HashingTF(hashSpaceSize)
val tf = hashingTF.transform(tokens)
// Build term frequency-inverse document frequency
val idfModel = new IDF(minDocFreq=minDocFreq).fit(tf)
val expandedText = idfModel.transform(tf)
(hashingTF, idfModel, expandedText)
}
“Thank for the order…”
[…,0,3.5,0,1,0,0.3,0,1.3,0,0,…]
Thank Order

24. Lego #3: Tf-IDF
def buildIDFModel(tokens: RDD[Seq[String]],
minDocFreq:Int = 4,
hashSpaceSize:Int = 1 << 10):
(HashingTF, IDFModel, RDD[Vector]) = {
// Hash strings into the given space
val hashingTF = new HashingTF(hashSpaceSize)
val tf = hashingTF.transform(tokens)
// Build term frequency-inverse document frequency
val idfModel = new IDF(minDocFreq=minDocFreq).fit(tf)
val expandedText = idfModel.transform(tf)
(hashingTF, idfModel, expandedText)
}
Hash words
into large
space
“Thank for the order…”
[…,0,3.5,0,1,0,0.3,0,1.3,0,0,…]
Thank Order

25. Lego #3: Tf-IDF
def buildIDFModel(tokens: RDD[Seq[String]],
minDocFreq:Int = 4,
hashSpaceSize:Int = 1 << 10):
(HashingTF, IDFModel, RDD[Vector]) = {
// Hash strings into the given space
val hashingTF = new HashingTF(hashSpaceSize)
val tf = hashingTF.transform(tokens)
// Build term frequency-inverse document frequency
val idfModel = new IDF(minDocFreq=minDocFreq).fit(tf)
val expandedText = idfModel.transform(tf)
(hashingTF, idfModel, expandedText)
}
Hash words
into large
space
Term freq scale
“Thank for the order…”
[…,0,3.5,0,1,0,0.3,0,1.3,0,0,…]
Thank Order

26. Lego #4: Build a model
def buildDLModel(train: Frame, valid: Frame,
epochs: Int = 10, l1: Double = 0.001, l2: Double = 0.0,
hidden: Array[Int] = Array[Int](200, 200))
(implicit h2oContext: H2OContext): DeepLearningModel = {
import h2oContext._
// Build a model
val dlParams = new DeepLearningParameters()
dlParams._destination_key = Key.make("dlModel.hex").asInstanceOf[Key[Frame]]
dlParams._train = train
dlParams._valid = valid
dlParams._response_column = 'target
dlParams._epochs = epochs
dlParams._l1 = l1
dlParams._hidden = hidden
// Create a job
val dl = new DeepLearning(dlParams)
val dlModel = dl.trainModel.get
// Compute metrics on both datasets
dlModel.score(train).delete()
dlModel.score(valid).delete()
dlModel
}
Deep Learning: Create
multi-layer feed forward
neural networks starting
with an input layer
followed by multiple
l a y e r s o f n o n l i n e a r
transformations

27. Assembly application
// Data load
val data = load(DATAFILE)
// Extract response spam or ham
val hamSpam = data.map( r => r(0))
val message = data.map( r => r(1))
// Tokenize message content
val tokens = tokenize(message)
// Build IDF model
var (hashingTF, idfModel, tfidf) = buildIDFModel(tokens)
// Merge response with extracted vectors
val resultRDD: SchemaRDD = hamSpam.zip(tfidf).map(v => SMS(v._1, v._2))
val table:DataFrame = resultRDD
// Split table
val keys = Array[String]("train.hex", "valid.hex")
val ratios = Array[Double](0.8)
val frs = split(table, keys, ratios)
val (train, valid) = (frs(0), frs(1))
table.delete()
// Build a model
val dlModel = buildDLModel(train, valid)

28. Assembly application
// Data load
val data = load(DATAFILE)
// Extract response spam or ham
val hamSpam = data.map( r => r(0))
val message = data.map( r => r(1))
// Tokenize message content
val tokens = tokenize(message)
// Build IDF model
var (hashingTF, idfModel, tfidf) = buildIDFModel(tokens)
// Merge response with extracted vectors
val resultRDD: SchemaRDD = hamSpam.zip(tfidf).map(v => SMS(v._1, v._2))
val table:DataFrame = resultRDD
// Split table
val keys = Array[String]("train.hex", "valid.hex")
val ratios = Array[Double](0.8)
val frs = split(table, keys, ratios)
val (train, valid) = (frs(0), frs(1))
table.delete()
// Build a model
val dlModel = buildDLModel(train, valid)
Data munging

29. Assembly application
// Data load
val data = load(DATAFILE)
// Extract response spam or ham
val hamSpam = data.map( r => r(0))
val message = data.map( r => r(1))
// Tokenize message content
val tokens = tokenize(message)
// Build IDF model
var (hashingTF, idfModel, tfidf) = buildIDFModel(tokens)
// Merge response with extracted vectors
val resultRDD: SchemaRDD = hamSpam.zip(tfidf).map(v => SMS(v._1, v._2))
val table:DataFrame = resultRDD
// Split table
val keys = Array[String]("train.hex", "valid.hex")
val ratios = Array[Double](0.8)
val frs = split(table, keys, ratios)
val (train, valid) = (frs(0), frs(1))
table.delete()
// Build a model
val dlModel = buildDLModel(train, valid)
Split dataset
Data munging

30. Assembly application
// Data load
val data = load(DATAFILE)
// Extract response spam or ham
val hamSpam = data.map( r => r(0))
val message = data.map( r => r(1))
// Tokenize message content
val tokens = tokenize(message)
// Build IDF model
var (hashingTF, idfModel, tfidf) = buildIDFModel(tokens)
// Merge response with extracted vectors
val resultRDD: SchemaRDD = hamSpam.zip(tfidf).map(v => SMS(v._1, v._2))
val table:DataFrame = resultRDD
// Split table
val keys = Array[String]("train.hex", "valid.hex")
val ratios = Array[Double](0.8)
val frs = split(table, keys, ratios)
val (train, valid) = (frs(0), frs(1))
table.delete()
// Build a model
val dlModel = buildDLModel(train, valid)
Split dataset
Build model
Data munging

31. Data exploration

32. Model evaluation
val trainMetrics = binomialMM(dlModel, train)
val validMetrics = binomialMM(dlModel, valid)

33. Model evaluation
val trainMetrics = binomialMM(dlModel, train)
val validMetrics = binomialMM(dlModel, valid)
Collect model
metrics

34. Spam predictor
def isSpam(msg: String,
dlModel: DeepLearningModel,
hashingTF: HashingTF,
idfModel: IDFModel,
hamThreshold: Double = 0.5):Boolean = {
val msgRdd = sc.parallelize(Seq(msg))
val msgVector: SchemaRDD = idfModel.transform(
hashingTF.transform (
tokenize (msgRdd)))
.map(v => SMS("?", v))
val msgTable: DataFrame = msgVector
msgTable.remove(0) // remove first column
val prediction = dlModel.score(msgTable)
prediction.vecs()(1).at(0) < hamThreshold
}

35. Spam predictor
def isSpam(msg: String,
dlModel: DeepLearningModel,
hashingTF: HashingTF,
idfModel: IDFModel,
hamThreshold: Double = 0.5):Boolean = {
val msgRdd = sc.parallelize(Seq(msg))
val msgVector: SchemaRDD = idfModel.transform(
hashingTF.transform (
tokenize (msgRdd)))
.map(v => SMS("?", v))
val msgTable: DataFrame = msgVector
msgTable.remove(0) // remove first column
val prediction = dlModel.score(msgTable)
prediction.vecs()(1).at(0) < hamThreshold
}
Prepared models

36. Spam predictor
def isSpam(msg: String,
dlModel: DeepLearningModel,
hashingTF: HashingTF,
idfModel: IDFModel,
hamThreshold: Double = 0.5):Boolean = {
val msgRdd = sc.parallelize(Seq(msg))
val msgVector: SchemaRDD = idfModel.transform(
hashingTF.transform (
tokenize (msgRdd)))
.map(v => SMS("?", v))
val msgTable: DataFrame = msgVector
msgTable.remove(0) // remove first column
val prediction = dlModel.score(msgTable)
prediction.vecs()(1).at(0) < hamThreshold
}
Prepared models
Default decision
threshold

37. Spam predictor
def isSpam(msg: String,
dlModel: DeepLearningModel,
hashingTF: HashingTF,
idfModel: IDFModel,
hamThreshold: Double = 0.5):Boolean = {
val msgRdd = sc.parallelize(Seq(msg))
val msgVector: SchemaRDD = idfModel.transform(
hashingTF.transform (
tokenize (msgRdd)))
.map(v => SMS("?", v))
val msgTable: DataFrame = msgVector
msgTable.remove(0) // remove first column
val prediction = dlModel.score(msgTable)
prediction.vecs()(1).at(0) < hamThreshold
}
Prepared models
Default decision
threshold
Scoring

38. Predict spam

39. Predict spam
isSpam("Michal, beer
tonight in MV?")

40. Predict spam
isSpam("Michal, beer
tonight in MV?")

41. Predict spam
isSpam("Michal, beer
tonight in MV?")
isSpam("We tried to contact
you re your reply
to our offer of a Video
Handset? 750
anytime any networks mins?
UNLIMITED TEXT?")

42. Predict spam
isSpam("Michal, beer
tonight in MV?")
isSpam("We tried to contact
you re your reply
to our offer of a Video
Handset? 750
anytime any networks mins?
UNLIMITED TEXT?")

43. Checkout H2O.ai Training Books
http://learn.h2o.ai/
Checkout H2O.ai Blog
http://h2o.ai/blog/
Checkout H2O.ai Youtube Channel
https://www.youtube.com/user/0xdata
Checkout GitHub
https://github.com/h2oai/sparkling-water
Meetups
https://meetup.com/
More info

44. Learn more at h2o.ai
Follow us at @h2oai
Thank you!
Sparkling Water is
open-source
ML application platform
combining
power of Spark and H2O