Mastering Scala Machine Learning

Alex Kozlov

• Publisher: Packt Publishing
• Year: 2016
• eBook ISBN: 9781785880889
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop

About This Book

• This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data
• Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning
• Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability

Who This Book Is For

Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required.

What You Will Learn

• Sharpen your functional programming skills in Scala using REPL
• Apply standard and advanced machine learning techniques using Scala
• Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data
• Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data
• Understand the principles of supervised and unsupervised learning in machine learning
• Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet
• Construct reliable and robust data pipelines and manage data in a data-driven enterprise
• Implement scalable model monitoring and alerts with Scala

In Detail

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.

Style and approach

This hands-on guide dives straight into implementing Scala for machine learning without delving much into mathematical proofs or validations. There are ample code examples and tricks that will help you sail through using the standard techniques and libraries. This book provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

Mastering Scala Machine Learning

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Table of Contents

Mastering Scala Machine Learning

Credits

About the Author

Acknowlegement

www.PacktPub.com

eBooks, discount offers, and more

Why subscribe?

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Exploratory Data Analysis

Getting started with Scala

Distinct values of a categorical field

Summarization of a numeric field

Grepping across multiple fields

Basic, stratified, and consistent sampling

Working with Scala and Spark Notebooks

Basic correlations

Summary

2. Data Pipelines and Modeling

Influence diagrams

Sequential trials and dealing with risk

Exploration and exploitation

Unknown unknowns

Basic components of a data-driven system

Data ingest

Data transformation layer

Data analytics and machine learning

UI component

Actions engine

Correlation engine

Monitoring

Optimization and interactivity

Feedback loops

Summary

3. Working with Spark and MLlib

Setting up Spark

Understanding Spark architecture

Task scheduling

Spark components

MQTT, ZeroMQ, Flume, and Kafka

HDFS, Cassandra, S3, and Tachyon

Mesos, YARN, and Standalone

Applications

Word count

Streaming word count

Spark SQL and DataFrame

ML libraries

SparkR

Graph algorithms – GraphX and GraphFrames

Spark performance tuning

Running Hadoop HDFS

Summary

4. Supervised and Unsupervised Learning

Records and supervised learning

Iris dataset

Labeled point

SVMWithSGD

Logistic regression

Decision tree

Bagging and boosting – ensemble learning methods

Unsupervised learning

Problem dimensionality

Summary

5. Regression and Classification

What regression stands for?

Continuous space and metrics

Linear regression

Logistic regression

Regularization

Multivariate regression

Heteroscedasticity

Regression trees

Classification metrics

Multiclass problems

Perceptron

Generalization error and overfitting

Summary

6. Working with Unstructured Data

Nested data

Other serialization formats

Hive and Impala

Sessionization

Working with traits

Working with pattern matching

Other uses of unstructured data

Probabilistic structures

Projections

Summary

7. Working with Graph Algorithms

A quick introduction to graphs

SBT

Graph for Scala

Adding nodes and edges

Graph constraints

JSON

GraphX

Who is getting e-mails?

Connected components

Triangle counting

Strongly connected components

PageRank

SVD++

Summary

8. Integrating Scala with R and Python

Integrating with R

Setting up R and SparkR

Linux

Mac OS

Windows

Running SparkR via scripts

Running Spark via R's command line

DataFrames

Linear models

Generalized linear model

Reading JSON files in SparkR

Writing Parquet files in SparkR

Invoking Scala from R

Using Rserve

Integrating with Python

Setting up Python

PySpark

Calling Python from Java/Scala

Using sys.process._

Spark pipe

Jython and JSR 223

Summary

9. NLP in Scala

Text analysis pipeline

Simple text analysis

MLlib algorithms in Spark

TF-IDF

LDA

Segmentation, annotation, and chunking

POS tagging

Using word2vec to find word relationships

A Porter Stemmer implementation of the code

Summary

10. Advanced Model Monitoring

System monitoring

Process monitoring

Model monitoring

Performance over time

Criteria for model retiring

A/B testing

Summary

Index

Mastering Scala Machine Learning

Copyright © 2016 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: June 2016

Production reference: 1220616

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78588-088-9

www.packtpub.com

Credits

Author

Alex Kozlov

Reviewer

Rok Kralj

Commissioning Editor

Dipika Gaonkar

Acquisition Editor

Kirk D'costa

Content Development Editor

Samantha Gonsalves

Technical Editor

Suwarna Patil

Copy Editor

Vibha Shukla

Project Coordinator

Sanchita Mandal

Proofreader

Safis Editing

Indexer

Mariammal Chettiyar

Graphics

Disha Haria

Production Coordinator

Arvindkumar Gupta

Cover Work

Arvindkumar Gupta

About the Author

Alex Kozlov is a multidisciplinary big data scientist. He came to Silicon Valley in 1991, got his Ph.D. from Stanford University under the supervision of Prof. Daphne Koller and Prof. John Hennessy in 1998, and has been around a few computer and data management companies since. His latest stint was with Cloudera, the leader in Hadoop, where he was one of the early employees and ended up heading the solution architects group on the West Coast. Before that, he spent time with an online advertising company, Turn, Inc.; and before that, he had the privilege to work with HP Labs researchers at HP Inc., and on data mining software at SGI, Inc. Currently, Alexander is the chief solutions architect at an enterprise security startup, E8 Security, where he came to understand the intricacies of catching bad guys in the Internet universe.

On the non-professional side, Alexander lives i...

Table of contents

• Mastering Scala Machine Learning