Book Image

Mastering Spark for Data Science

By : Andrew Morgan, Antoine Amend, Matthew Hallett, David George
Book Image

Mastering Spark for Data Science

By: Andrew Morgan, Antoine Amend, Matthew Hallett, David George

Overview of this book

Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Table of Contents (22 chapters)
Mastering Spark for Data Science
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Twitter and the Godwin point


With our text content properly cleaned up, we can feed a Word2Vec algorithm and attempt to understand the words in their actual context.

Learning context

As it says on the tin, the Word2Vec algorithm transforms a word into a vector. The idea is that similar words will be embedded into similar vector spaces and, as such, will look close to one another contextually.

Note

More information about Word2Vec algorithm can be found at https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf.

Well integrated into Spark, a Word2Vec model can be trained as follows:

import org.apache.spark.mllib.feature.Word2Vec

val corpusRDD = tweetRDD
   .map(_.body.split("\\s").toSeq)
   .filter(_.distinct.length >= 4)

val model = new Word2Vec().fit(corpusRDD)

Here we extract each tweet as a sequence of words, only keeping records with at least 4 distinct words. Note that the list of all words needs to...