Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Normalizing data with Spark


In this recipe, we normalizing (scaling) the data prior to importing the into an ML algorithm. There are a good number of ML algorithms such as Support Vector Machine (SVM) that work with scaled input vectors rather than with the raw values.

How to do it...

  1. Go to the UCI Machine Learning Repository and download the http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data file.
  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
  1. Set up the package location where the program will reside:
package spark.ml.cookbook.chapter4
  1. Import the necessary packages for the Spark session to gain access to the cluster and log4j.Logger to reduce the amount of output produced by Spark:
import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.feature.MinMaxScaler
  1. Define a method to parse wine data into a tuple:
def parseWine(str: String): (Int, Vector...