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

A quick introduction to graphs


What is a graph? A graph is a set of vertices where some pairs of these vertices are linked with edges. If every vertex is linked with every other vertex, we say the graph is a complete graph. On the contrary, if it has no edges, the graph is said to be empty. These are, of course, extremes that are rarely encountered in practice, as graphs have varying degrees of density; the more edges it has proportional to the number of vertices, the more dense we say it is.

Depending on what algorithms we intend to run on a graph and how dense is it expected to be, we can choose how to appropriately represent the graph in memory. If the graph is really dense, it pays off to store it as a square N x N matrix, where 0 in the nth row and mth column means that the n vertex is not connected to the m vertex. A diagonal entry expresses a node connection to itself. This representation is called the adjacency matrix.

If there are not many edges and we need to traverse the whole edge...