This document summarizes an automated machine learning framework for customer journey analysis. The framework uses GraphX to process event streams and connect discrete interactions into user journeys. It then applies feature engineering techniques like binning and frequent item counting to transform journey data into a format suitable for model building. Multiple models can be configured and built in Spark MLlib, with the best model selected using performance metrics. The selected model is stored for serving predictions to applications through a prediction server. The goal is to automate the full machine learning cycle from raw event streams to predictive models.

1. Automated Machine
Learning @[24]7
Sourabh Chaki

2. Anticipate, Simplify, Learn
A Proactive Framework for Intuitive Customer Care

3. 3
Visitor
Visitor
Caller
Homepage Product Page Cart Purchased
Chat Offered Chat Accepted Chat Started Chat Ended
Call Connected Call Ended Call Stats
Event
Discrete action
Interaction
Group of discrete events
Product Page
Web
Chat
Voice

4. Graph Problem
Web
Voice
Chat
Scattered
Interactions
Finding
Connected
Components
Visitor 1 Journey
Visitor 2 Journey

5. Graph processing in Hadoop
• HashToMin for Connected Component
– http://arxiv.org/pdf/1203.5387.pdf
• Log(n) mapreduce iterations for connecting n diameter
graph
• Challenges:
– Thinking graph problem in map and reduce
– Not fit for iterative algorithms

6. GraphX
• Spark fits for iterative programming
• In-memory data
• Think as edges and vertices
• High level APIs for graph processing

7. Connected Component in GraphX
• Vertices >> Edges
– Graph from only edges
– Interactions as external KVP RDD
• val cc = graph.connectedComponents()
• Join cc.vertices with interactions
• Group interactions with leaders

8. Join
• Broadcast join
• Hash Partition Join
• Adaptive
– Small data: Broadcast
– Large data: HashPartition

9. Memory Share
• Default shuffle memory 20%
– Low for shuffle heavy application
• Shuffle memory: 40%, storage memory 40%

10. Performance Gain
0
10
20
30
40
50
60
2 4 8
Mins
Graph Diameter
Hadoop Vs Spark (Mins), 350M events
Hadoop
GraphX

11. Interactions to Journey
web
Voice
Chat
Events
Connecting
User Journeys
User Journey Journey Views

12. User Journey to Model
Journey
View
Pre-processing
and model building
Model

13. Feature Engineering
• Training Set/Test Set
• Balanced Sampling
• Frequent Items
• Quantile Bins
• Transformation Function

14. Feature Engineering Config
trainTest=60,40
#features
catVarIndices=1,2
contVarIndices=3,4
labelIndex=0
#transformation
catVar.1.bin=5
catVar.2.bin=4
contVar.3.bin=5
contVar.4.bin=6
#sampling
label.1.wt=10
label.2.wt=100

15. Column wise data analysis
Quantile
TopK
Frequent
 Sum (set1 , set2) = Sum(set1) + Sum(set2)
Quantile(Set1 + Set2) = Quantile(Set1) +
Quantile(Set2)
TopKFrequent(set1, set2) =
TopKFrequent(set1) + TopKFrequent(set2)
Quantile
TopK
Frequent
Quantile

16. Problems
• Non distributed
• Need data shuffle
• Shuffle on different columns
• High disk and network IO

17. Algebird with Spark
F(set1 + set2) ≈ F(set1) + F(set2)
• QTree
• CountMinSketch
https://github.com/twitter/algebird

18. Binning Product Price
Product price => low / high value product
val aggregator= new QTreeSemigroup[Double](8)
val qtree = journeyViewRDD
.map(row=>QTree(row(productPriceIndex)))
.reduce(aggregator.plus(_,_) )
val median = qtree.quantileBounds(0.5)._1

19. Binning Product Price
Product price => low / high value product
class TransformationFunc(val median:Double) extends Serializable {
def transform (value:Double): String = {
if(value<median) "low"
else "high"
}
}
val transFunc = new TransformationFunc(median)
val priceCat = transFunc.transform(productPrice)

20. Automated Feature Engineering
Journey
View
Data Set and
Transformation Function
Feature Engineering
Configs

21. Model Building
• Spark MLlib
• Random Forest for classification
• Model Testing
• Performance Metrics

22. Model Config
#model
model=randomforest
randomforest.algo=gini
randomforest.bin=20
randomforest.classes=2
randomforest.depth=10
randomforest.numTrees=10
randomforest.featureSubsetStrategy=auto
#model metric
modelMetric=ROC

23. Automated Model Building
Multiple
Models
Model Configs
Data Set and
Transformation Function
Model
Testing
Best Model
Model Building

24. Prediction Entity
• Transformation Function Object
• Random Forest Model Object
class PredictionEntity(model:RandomForestModel,
tansformationFunc:TransformationFunc)
extends Serializable {
def predict(vector:Vector):Double = {
//transform the vector using transformationFunc
//predict using model
}
}

25. Prediction outside Spark
• Need export
– No PMML support
• Model store
• Synchronous prediction call

26. Prediction outside Spark
Model Store Prediction
Web
Voice
Chat
Wrapper
over MySql
Application
Server
Serialized Byte
Array
Prediction Entity

27. Machine Learning Cycle
Web
Voice
Chat
Event
Stream
GraphX Mllib
Journey
View
Model Model
Store
Prediction
Server
Configurations

28. Thank You