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Scala for Data Science
Scala for Data Science

Scala for Data Science: Leverage the power of Scala with different tools to build scalable, robust data science applications

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Scala for Data Science

Chapter 2. Manipulating Data with Breeze

Data science is, by and large, concerned with the manipulation of structured data. A large fraction of structured datasets can be viewed as tabular data: each row represents a particular instance, and columns represent different attributes of that instance. The ubiquity of tabular representations explains the success of spreadsheet programs like Microsoft Excel, or of tools like SQL databases.

To be useful to data scientists, a language must support the manipulation of columns or tables of data. Python does this through NumPy and pandas, for instance. Unfortunately, there is no single, coherent ecosystem for numerical computing in Scala that quite measures up to the SciPy ecosystem in Python.

In this chapter, we will introduce Breeze, a library for fast linear algebra and manipulation of data arrays as well as many other features necessary for scientific computing and data science.

Code examples

The easiest way to access the code examples in this book is to clone the GitHub repository:

$ git clone 'https://github.com/pbugnion/s4ds'

The code samples for each chapter are in a single, standalone folder. You may also browse the code online on GitHub.

Installing Breeze

If you have downloaded the code examples for this book, the easiest way of using Breeze is to go into the chap02 directory and type sbt console at the command line. This will open a Scala console in which you can import Breeze.

If you want to build a standalone project, the most common way of installing Breeze (and, indeed, any Scala module) is through SBT. To fetch the dependencies required for this chapter, copy the following lines to a file called build.sbt, taking care to leave an empty line after scalaVersion:

scalaVersion := "2.11.7"

libraryDependencies ++= Seq(
  "org.scalanlp" %% "breeze" % "0.11.2",
  "org.scalanlp" %% "breeze-natives" % "0.11.2"
)

Open a Scala console in the same directory as your build.sbt file by typing sbt console in a terminal. You can check that Breeze is working correctly by importing Breeze from the Scala prompt:

scala> import breeze.linalg._
import breeze.linalg._
...

Getting help on Breeze

This chapter gives a reasonably detailed introduction to Breeze, but it does not aim to give a complete API reference.

To get a full list of Breeze's functionality, consult the Breeze Wiki page on GitHub at https://github.com/scalanlp/breeze/wiki. This is very complete for some modules and less complete for others. The source code (https://github.com/scalanlp/breeze/) is detailed and gives a lot of information. To understand how a particular function is meant to be used, look at the unit tests for that function.

Basic Breeze data types

Breeze is an extensive library providing fast and easy manipulation of arrays of data, routines for optimization, interpolation, linear algebra, signal processing, and numerical integration.

The basic linear algebra operations underlying Breeze rely on the netlib-java library, which can use system-optimized BLAS and LAPACK libraries, if present. Thus, linear algebra operations in Breeze are often extremely fast. Breeze is still undergoing rapid development and can, therefore, be somewhat unstable.

Vectors

Breeze makes manipulating one- and two-dimensional data structures easy. To start, open a Scala console through SBT and import Breeze:

$ sbt console
scala> import breeze.linalg._
import breeze.linalg._

Let's dive straight in and define a vector:

scala> val v = DenseVector(1.0, 2.0, 3.0)
breeze.linalg.DenseVector[Double] = DenseVector(1.0, 2.0, 3.0)

We have just defined a three-element vector, v. Vectors are just one-dimensional arrays of data exposing methods...

An example – logistic regression

Let's now imagine we want to build a classifier that takes a person's height and weight and assigns a probability to their being Male or Female. We will reuse the height and weight data introduced earlier in this chapter. Let's start by plotting the dataset:

An example – logistic regression

Height versus weight data for 181 men and women

There are many different algorithms for classification. A first glance at the data shows that we can, approximately, separate men from women by drawing a straight line across the plot. A linear method is therefore a reasonable initial attempt at classification. In this section, we will use logistic regression to build a classifier.

A detailed explanation of logistic regression is beyond the scope of this book. The reader unfamiliar with logistic regression is referred to The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman. We will just give a brief summary here.

Logistic regression estimates the probability of a given...

Code examples


The easiest way to access the code examples in this book is to clone the GitHub repository:

$ git clone 'https://github.com/pbugnion/s4ds'

The code samples for each chapter are in a single, standalone folder. You may also browse the code online on GitHub.

Installing Breeze


If you have downloaded the code examples for this book, the easiest way of using Breeze is to go into the chap02 directory and type sbt console at the command line. This will open a Scala console in which you can import Breeze.

If you want to build a standalone project, the most common way of installing Breeze (and, indeed, any Scala module) is through SBT. To fetch the dependencies required for this chapter, copy the following lines to a file called build.sbt, taking care to leave an empty line after scalaVersion:

scalaVersion := "2.11.7"

libraryDependencies ++= Seq(
  "org.scalanlp" %% "breeze" % "0.11.2",
  "org.scalanlp" %% "breeze-natives" % "0.11.2"
)

Open a Scala console in the same directory as your build.sbt file by typing sbt console in a terminal. You can check that Breeze is working correctly by importing Breeze from the Scala prompt:

scala> import breeze.linalg._
import breeze.linalg._

Getting help on Breeze


This chapter gives a reasonably detailed introduction to Breeze, but it does not aim to give a complete API reference.

To get a full list of Breeze's functionality, consult the Breeze Wiki page on GitHub at https://github.com/scalanlp/breeze/wiki. This is very complete for some modules and less complete for others. The source code (https://github.com/scalanlp/breeze/) is detailed and gives a lot of information. To understand how a particular function is meant to be used, look at the unit tests for that function.

Basic Breeze data types


Breeze is an extensive library providing fast and easy manipulation of arrays of data, routines for optimization, interpolation, linear algebra, signal processing, and numerical integration.

The basic linear algebra operations underlying Breeze rely on the netlib-java library, which can use system-optimized BLAS and LAPACK libraries, if present. Thus, linear algebra operations in Breeze are often extremely fast. Breeze is still undergoing rapid development and can, therefore, be somewhat unstable.

Vectors

Breeze makes manipulating one- and two-dimensional data structures easy. To start, open a Scala console through SBT and import Breeze:

$ sbt console
scala> import breeze.linalg._
import breeze.linalg._

Let's dive straight in and define a vector:

scala> val v = DenseVector(1.0, 2.0, 3.0)
breeze.linalg.DenseVector[Double] = DenseVector(1.0, 2.0, 3.0)

We have just defined a three-element vector, v. Vectors are just one-dimensional arrays of data exposing methods...

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Key benefits

  • A complete guide for scalable data science solutions, from data ingestion to data visualization
  • Deploy horizontally scalable data processing pipelines and take advantage of web frameworks to build engaging visualizations
  • Build functional, type-safe routines to interact with relational and NoSQL databases with the help of tutorials and examples provided

Description

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.

Who is this book for?

If you are a Scala developer or data scientist, or if you want to enter the field of data science, then this book will give you all the tools you need to implement data science solutions.

What you will learn

  • Transform and filter tabular data to extract features for machine learning
  • Implement your own algorithms or take advantage of MLLib's extensive suite of models to build distributed machine learning pipelines
  • Read, transform, and write data to both SQL and NoSQL databases in a functional manner
  • Write robust routines to query web APIs
  • Read data from web APIs such as the GitHub or Twitter API
  • Use Scala to interact with MongoDB, which offers high performance and helps to store large data sets with uncertain query requirements
  • Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations
  • Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive
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Table of Contents

16 Chapters
1. Scala and Data Science Chevron down icon Chevron up icon
2. Manipulating Data with Breeze Chevron down icon Chevron up icon
3. Plotting with breeze-viz Chevron down icon Chevron up icon
4. Parallel Collections and Futures Chevron down icon Chevron up icon
5. Scala and SQL through JDBC Chevron down icon Chevron up icon
6. Slick – A Functional Interface for SQL Chevron down icon Chevron up icon
7. Web APIs Chevron down icon Chevron up icon
8. Scala and MongoDB Chevron down icon Chevron up icon
9. Concurrency with Akka Chevron down icon Chevron up icon
10. Distributed Batch Processing with Spark Chevron down icon Chevron up icon
11. Spark SQL and DataFrames Chevron down icon Chevron up icon
12. Distributed Machine Learning with MLlib Chevron down icon Chevron up icon
13. Web APIs with Play Chevron down icon Chevron up icon
14. Visualization with D3 and the Play Framework Chevron down icon Chevron up icon
A. Pattern Matching and Extractors Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
(5 Ratings)
5 star 80%
4 star 0%
3 star 20%
2 star 0%
1 star 0%
adnan baloch May 05, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
One of the hottest jobs these days is that of the data scientist. It makes sense given the explosion of data generated by the online activities of millions of internet users and collected by online businesses and social media websites. As the author of this book explains, data scientists need to be conversant in three areas at once: programming, statistics/numerical algorithms and the ability to ask the right questions that will help in making decisions crucial to expanding a business and keeping it competitive. This book deals with the first of these essential skills: programming. Scala is a functional programming language with powerful parallel computing capabilities. The functional part of the language ensures that code written in Scala is terse and avoids common bugs that are the major source of headaches in traditional languages like Python or Java. The one place where Scala lags is in the availability of mature libraries. Still, the author discusses several good Scala libraries that make the Scala programmer's job easy so she can focus on the actual data science. Breeze and Breeze-viz are put to use in manipulating arrays of data and plotting simple graphs respectively. Parallel collections are explained intuitively so that anyone without any experience of parallel computation will find it useful. Futures make it possible to add further concurrency to Scala based projects by freeing the main thread from blocking events like waiting to receive data from a web page.Databases form the core of data storage in any data focused programming solution. The author shows how to write a functional wrapper for JDBC and also discusses a popular functional wrapper called Slick so the readers will be equipped to handle both scenarios depending on their needs. Gathering data from the web can hardly work without an understanding of interfacing with APIs. The author takes a very practical approach in exploring this crucial aspect by querying the Github API and storing the data in MongoDB. Furthermore, readers get to see how to create their own simple web API. Sooner or later, data scientists have to turn to distributed computing for the horsepower needed to complete their complex calculations. Actor based concurrency using Akka fills this gap and the author gives it an excellent treatment in a dedicated chapter. Machine learning is discussed using MLlib but a good conceptual understanding of ML is needed for this chapter. The uninitiated are forewarned: don't expect the author to teach machine learning in a single chapter. For me, the most exciting two chapters are the ones that use the Play framework with D3.js to build a single page app. This represents true empowerment because it enables budding data scientists to share their fruits of labor with the entire web community in a visually captivating way. In short, data scientists wondering about Scala's effectiveness as a great tool for data science need only skim through this book. They won't be disappointed.
Amazon Verified review Amazon
Bill Jones Apr 23, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The good: The book covers using Scala with various tools and provides use cases, it dives in but not deep. In my opinion it is a great beginner book to help you get started with Scala, but you'll want to pick up another title after this for continued learning.The bad: I really wished it would have dived in deeper and focused less on integrating multiple say DB platforms, but overall not enough to make me hate the book.
Amazon Verified review Amazon
Amazon Customer Feb 21, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good. You can also buy Scala for Machine Learning
Amazon Verified review Amazon
Timothy J. Whittaker Apr 09, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I spent a lot of time looking for a book like this. The other reviewer is correct, there is very little on actual statistical learning in this text, but this is not the author's aim. To me, this is more about awareness of some great Scala (and Java) libraries (with application) that any data scientist should find useful. The definition of data science taken by this book is probably the broadest I have seen - there is something worthwhile in every single chapter of this book.
Amazon Verified review Amazon
Duncan W. Robinson Mar 21, 2016
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Scala for Data Science was a fairly good introduction for me to applied Scala applications and interoperability. Working through a few examples in this book proved to be my first foray into using Scala. In my opinion, the book seemed a bit light on techniques for statistical learning, but was rich in tools showing how to Scala with JSON, APIs, SQL, MongoDB, and Spark.
Amazon Verified review Amazon
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