Interactive Sparkling Water session by Michal Malohlava - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. Building Machine
Learning Applications
with Sparkling Water
04/15/2015 Meetup
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. …with Spark and H2O

9. 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

10. 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

11. Ensembles
Deep Neural Networks
• Generalized Linear Models : Binomial, Gaussian,
Gamma, Poisson and Tweedie
• Cox Proportional Hazards Models
• Naïve Bayes
• Distributed Random Forest : Classification or
regression models
• Gradient Boosting Machine : Produces an
ensemble of decision trees with increasing refined
approximations
• Deep learning : Create multi-layer feed forward
neural networks starting with an input layer
followed by multiple layers of nonlinear
transformations
Statistical Analysis
Dimensionality
Reduction
Anomaly
Detection
• K-means : Partitions observations into k
clusters/groups of the same spatial size
• Principal Component Analysis : Linearly
transforms correlated variables to
independent components
• Autoencoders: Find outliers using a
nonlinear dimensionality reduction using
deep learning
Clustering
Supervised
Learning
Unsupervised
Learning

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

14. Sparkling Water Design
spark-submit
Spark
Master
JVM
Spark
Worker
JVM
Spark
Worker
JVM
Spark
Worker
JVM
Sparkling Water Cluster
Spark
Executor
JVM
H2O
Spark
Executor
JVM
H2O
Spark
Executor
JVM
H2O
Sparkling
App
implements
?
Regular Spark application
containing also
Sparkling Water
classes

15. Data Distribution
H2O
H2O
H2O
Sparkling Water Cluster
Spark Executor JVM
Data
Source
(e.g.
HDFS)
H2O
RDD
Spark Executor JVM
Spark Executor JVM
Spark
RDD
RDDs and DataFrames
share same memory
space
toRDD
toH2OFrame

16. Lets build
an application !

17. OR
Detect spam text messages

18. Data example
case class SMS(target: String, fv: Vector)
ham / spam feature vector

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

20. Application
environment
sparkling-shell

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)
}
Produces [response,message]

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
}
Message Bag of words

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)
}
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
Bag of words
hashing
functions
term freq
model
Feature
vectors representing text

24. 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
H2O model
builder API
Do final scoring
on train/test data

25. 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

26. Data exploration

27. Model evaluation
val trainMetrics = binomialMM(dlModel, train)
val validMetrics = binomialMM(dlModel, valid)
Collect Model
Metrics

28. 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

29. 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?")

30. Interactions with
application from R
You need to install
H2O’s R-package from:
http://h2o-release.s3.amazonaws.com/h2o-dev/master/
1109/index.html#R

31. Use app from R
Collect Model
Metrics

32. Where is the code?
https://github.com/h2oai/sparkling-water/
blob/master/examples/scripts/

33. Sparkling Water Download
http://h2o.ai/download/
http://h2o-release.s3.amazonaws.com/
sparkling-water/master/93/index.html

34. 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

35. 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