Book Image

Mastering Scala Machine Learning

By : Alex Kozlov
Book Image

Mastering Scala Machine Learning

By: Alex Kozlov

Overview of this book

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.
Table of Contents (17 chapters)
Mastering Scala Machine Learning
Credits
About the Author
Acknowlegement
www.PacktPub.com
Preface
10
Advanced Model Monitoring
Index

Chapter 7. Working with Graph Algorithms

In this chapter, I'll delve into graph libraries and algorithm implementations in Scala. In particular, I will introduce Graph for Scala (http://www.scala-graph.org), an open source project that was started in 2011 in the EPFL Scala incubator. Graph for Scala does not support distributed computing yet—the distributed computing aspects of popular graph algorithms is available in GraphX, which is a part of MLlib library that is part of Spark project (http://spark.apache.org/docs/latest/mllib-guide.html). Both, Spark and MLlib were started as class projects at UC Berkeley around or after 2009. I considered Spark in Chapter 3, Working with Spark and MLlib and introduced an RDD. In GraphX, a graph is a pair of RDDs, each of which is partitioned among executors and tasks, represents vertices and edges in a graph.

In this chapter, we will cover the following topics:

  • Configuring Simple Build Tool (SBT) to use the material in this chapter interactively

  • Learning...