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Scala for Machine Learning, Second Edition

Scala for Machine Learning, Second Edition

By : R. Nicolas
4.5 (2)
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Scala for Machine Learning, Second Edition

Scala for Machine Learning, Second Edition

4.5 (2)
By: R. Nicolas

Overview of this book

The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits. You start by learning data preprocessing and filtering techniques. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. Further, it covers the discriminative algorithms such as linear, logistic regression with regularization, kernelization, support vector machines, neural networks, and deep learning. You’ll move on to evolutionary computing, multibandit algorithms, and reinforcement learning. Finally, the book includes a comprehensive overview of parallel computing in Scala and Akka followed by a description of Apache Spark and its ML library. With updated codes based on the latest version of Scala and comprehensive examples, this book will ensure that you have more than just a solid fundamental knowledge in machine learning with Scala.
Table of Contents (21 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Started
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1. Getting Started
Mathematical notations for the curious
Why machine learning?
Why Scala?
Model categorization
Taxonomy of machine learning algorithms
Leveraging Java libraries
Tools and frameworks
Source code
Let's kick the tires
Summary
2
2. Data Pipelines
2. Data Pipelines
Modeling
Defining a methodology
Monadic data transformation
Workflow computational model
Profiling data
Assessing a model
Summary
3
3. Data Preprocessing
3. Data Preprocessing
Time series in Scala
Moving averages
Fourier analysis
The discrete Kalman filter
Alternative preprocessing techniques
Summary
4
4. Unsupervised Learning
4. Unsupervised Learning
K-mean clustering
Expectation-Maximization (EM)
Summary
5
5. Dimension Reduction
5. Dimension Reduction
Challenging model complexity
The divergences
Principal components analysis (PCA)
Nonlinear models
Summary
6
6. Naïve Bayes Classifiers
6. Naïve Bayes Classifiers
Probabilistic graphical models
Naïve Bayes classifiers
Multivariate Bernoulli classification
Naïve Bayes and text mining
Pros and cons
Summary
7
7. Sequential Data Models
7. Sequential Data Models
Markov decision processes
The hidden Markov model (HMM)
Conditional random fields
Regularized CRF and text analytics
Comparing CRF and HMM
Performance consideration
Summary
8
8. Monte Carlo Inference
8. Monte Carlo Inference
The purpose of sampling
Gaussian sampling
Monte Carlo approximation
Bootstrapping with replacement
Markov Chain Monte Carlo (MCMC)
Summary
9
9. Regression and Regularization
9. Regression and Regularization
Linear regression
Regularization
Numerical optimization
Logistic regression
Summary
10
10. Multilayer Perceptron
10. Multilayer Perceptron
Feed-forward neural networks (FFNN)
The multilayer perceptron (MLP)
Evaluation
Benefits and limitations
Summary
11
11. Deep Learning
11. Deep Learning
Sparse autoencoder
Restricted Boltzmann Machines (RBMs)
Convolution neural networks
12
12. Kernel Models and SVM
12. Kernel Models and SVM
Kernel functions
The support vector machine (SVM)
Performance considerations
Summary
13
13. Evolutionary Computing
13. Evolutionary Computing
Evolution
Genetic algorithms and machine learning
Genetic algorithm components
Implementation
GA for trading strategies
Advantages and risks of genetic algorithms
Summary
14
14. Multiarmed Bandits
14. Multiarmed Bandits
K-armed bandit
Thompson sampling
Upper bound confidence
Summary
15
15. Reinforcement Learning
15. Reinforcement Learning
Reinforcement learning
Learning classifier systems
Summary
16
16. Parallelism in Scala and Akka
16. Parallelism in Scala and Akka
Overview
Scala
Scalability with Actors
Akka
Summary
17
17. Apache Spark MLlib
17. Apache Spark MLlib
Overview
Apache Spark core
MLlib library
Reusable ML pipelines
Extending Spark
Streaming engine
Performance evaluation
Pros and cons
Summary
18
A. Basic Concepts
A. Basic Concepts
Scala programming
Mathematics
Finances 101
Suggested online courses
References
19
B. References
B. References
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
20
Index
Index

Tools and frameworks

Before getting your hands dirty, you need to download and deploy the minimum set of tools and libraries; there is no need to reinvent the wheel, after all. A few key components have to be installed in order to compile and run the source code described throughout this book. We will focus on open source and commonly available libraries, although you are invited to experiment with the equivalent tools of your choice. The learning curve for the frameworks described here is minimal.

Java

The code described in the book has been tested with JDK 1.7.0_45 and JDK 1.8.0_25 on Windows x64 and MacOS X x64. You need to install the Java Development Kit if you have not already done so. Finally, the environment variables JAVA_HOME, PATH, and CLASSPATH have to be updated accordingly.

Scala

The code has been tested with Scala 2.11.4 and 2.11.8. We recommend using Scala version 2.11.4 or higher with SBT 0.13.1 or higher. Let's assume that the Scala runtime (REPL) and libraries have been properly installed and that the environment variables SCALA_HOME, and PATH have been updated.

The Scala standard library can be downloaded as binaries or as part of the Typesafe Activator tool by visiting http://www.scala-lang.org/download/.

Eclipse Scala IDE

The description and installation instructions for the Eclipse Scala IDE version 4.0 and higher is available at http://scala-ide.org/docs/user/gettingstarted.html.

IntelliJ IDEA Scala plugin

You can also download the IntelliJ IDEA Scala plugin version 13 or higher from the JetBrains website at http://confluence.jetbrains.com/display/SCA/.

Simple build tool

The ubiquitous Simple Build Tool (SBT) will be our primary building engine. It can be downloaded as part of the Typesafe activator or directly from http://www.scala-sbt.org/download.html.

The syntax of the build file sbt/build.sbt conforms to version 0.13 and is used to compile and assemble the source code presented throughout this book. To build Scala for machine learning, do the following:

  • Set the maximum size for the JVM heap to 2058 Mbytes or higher and the permanent memory to 512 Mbytes or higher (that is, -Xmx4096m -Xms512m -XX:MaxPermSize=512m)
  • To build the Scala for machine learning library package: $(ROOT)/sbt clean publish-local
  • To build the package including test and resource files: $(ROOT)/sbt clean package
  • To generate Scala doc for the library: $(ROOT)/sbt doc
  • To generate Scala doc for the example: $(ROOT)/sbt test:doc
  • To generate report for compliance to Scala style guide: $(ROOT)/sbt scalastyle
  • To compile all examples: $(ROOT)/sbt test:compile

Apache Commons Math

Apache Commons Math is a Java library for numerical processing, algebra, statistics, and optimization [1:6].

Description

This is a lightweight library that provides developers with a foundation of small, ready-to-use Java classes that can be easily weaved into a machine learning problem. The examples used throughout the book require version 3.5 or higher.

The math library supports the following:

  • Functions, differentiation, integral, and ordinary differential equations
  • Statistics distributions
  • Linear and non-linear optimization
  • Dense and sparse vectors and matrix
  • Curve fitting, correlation, and regressio

For more information, visit http://commons.apache.org/proper/commons-math.

Licensing

We need Apache Public License 2.0; the terms are available at https://www.apache.org/licenses/LICENSE-2.0.

Installation

The installation and deployment of the Apache Commons Math library are quite simple. The steps are as follows:

  1. Go to the download page at http://commons.apache.org/proper/commons-math/download_math.cgi.
  2. Download the latest .jar files in the binary section, commons-math3-3.6-bin.zip (for version 3.6, for instance).
  3. Unzip and install the .jar file.
  4. Add commons-math3-3.6.jar to the CLASSPATH, as follows:
    • For macOS X:
             export CLASSPATH=$CLASSPATH:/Commons_Math_path
                        /commons-math3-3.6.jar
    • For Windows:

      Go to System property | Advanced system settings | Advanced | Environment variables and then edit the entry CLASSPATH variable.

  5. Add the commons-math3-3.6.jar file to your IDE environment if needed:
    • Eclipse Scala IDE: Project | Properties | Java Build Path | Libraries | Add External JARs
    • IntelliJ IDEA: File | Project Structure | Project Settings | Libraries |

the source commons-math3-3.6-src.zip from the source section.

JFreeChart

JFreeChart is an open source chart and plotting java library widely used in the Java programmer community. It was originally created by David Gilbert [1:8].

Description

The library supports a variety of configurable plots and charts (scatter, dial, pie, area, bar, box and whisker, stacked, and 3D). We use JFreeChart to display the output of data processing and algorithm throughout the book, but you are encouraged to explore this great library on your own, as time permits.

Licensing

It is distributed under the terms of the GNU Lesser General Public License (LGPL), which permits its use in proprietary applications.

Installation

To install and deploy JFreeChart, perform the following steps:

  1. Visit http://www.jfree.org/jfreechart/.
  2. Download the latest version from Source Forge: https://sourceforge.net/projects/jfreechart/files/.
  3. Unzip and deploy the .jar file.
  4. Add jfreechart-1.0.17.jar (for version 1.0.17) to the CLASSPATH, as follows:
    • For macOS X:
      export CLASSPATH=$CLASSPATH:/JFreeChart_path/jfreechart-1.0.17.jar
    • For Windows:

      Go to System property | Advanced system settings | Advanced | Environment variables and then edit the entry CLASSPATH variable.

  5. Add the jfreechart-1.0.17.jar file to your IDE environment:
    • Eclipse Scala IDE: Project | Properties | Java Build Path | Libraries | Add External JARs
    • IntelliJ IDEA: File | Project Structure | Project Settings | Libraries | +

Other libraries and frameworks

Libraries and tools that are specific to a single chapter are introduced along with the topic. Scalable frameworks are presented in the last chapter along with instructions for downloading them. Libraries related to the conditional random fields and support vector machines are described in their respective chapters.

Note

Why aren't we using Scala algebra and Scala numerical libraries?

Libraries such as Breeze, ScalaNLP, and Algebird are interesting Scala frameworks for linear algebra, numerical analysis, and machine learning. They provide even the most seasoned Scala programmer with a high-quality layer of abstraction. However, this book is designed as a tutorial that allows developers to write algorithms from the ground up using existing or legacy java libraries [1:9].

End of Section 8
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