Book Image

Scala for Data Science

By : Pascal Bugnion
Book Image

Scala for Data Science

By: Pascal Bugnion

Overview of this book

Scala is a multi-paradigm programming language (it supports both object-oriented and functional programming) and scripting language used to build applications for the JVM. Languages such as R, Python, Java, and so on are mostly used for data science. It is particularly good at analyzing large sets of data without any significant impact on performance and thus Scala is being adopted by many developers and data scientists. Data scientists might be aware that building applications that are truly scalable is hard. Scala, with its powerful functional libraries for interacting with databases and building scalable frameworks will give you the tools to construct robust data pipelines. This book will introduce you to the libraries for ingesting, storing, manipulating, processing, and visualizing data in Scala. Packed with real-world examples and interesting data sets, this book will teach you to ingest data from flat files and web APIs and store it in a SQL or NoSQL database. It will show you how to design scalable architectures to process and modelling your data, starting from simple concurrency constructs such as parallel collections and futures, through to actor systems and Apache Spark. As well as Scala’s emphasis on functional structures and immutability, you will learn how to use the right parallel construct for the job at hand, minimizing development time without compromising scalability. Finally, you will learn how to build beautiful interactive visualizations using web frameworks. This book gives tutorials on some of the most common Scala libraries for data science, allowing you to quickly get up to speed with building data science and data engineering solutions.
Table of Contents (22 chapters)
Scala for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Towards re-usable code


In the previous section, we performed all of the computation in a single script. While this is fine for data exploration, it means that we cannot reuse the logistic regression code that we have built. In this section, we will start the construction of a machine learning library that you can reuse across different projects.

We will factor the logistic regression algorithm out into its own class. We construct a LogisticRegression class:

import breeze.linalg._
import breeze.numerics._
import breeze.optimize._

class LogisticRegression(
    val training:DenseMatrix[Double], 
    val target:DenseVector[Double])
{

The class takes, as input, a matrix representing the training set and a vector denoting the target variable. Notice how we assign these to vals, meaning that they are set on class creation and will remain the same until the class is destroyed. Of course, the DenseMatrix and DenseVector objects are mutable, so the values that training and target point to might change...