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.

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Table of Contents (20 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Practical Machine Learning with Spark Using Scala
Practical Machine Learning with Spark Using Scala
Introduction
Downloading and installing the JDK
Downloading and installing IntelliJ
Downloading and installing Spark
Configuring IntelliJ to work with Spark and run Spark ML sample codes
Running a sample ML code from Spark
Identifying data sources for practical machine learning
Running your first program using Apache Spark 2.0 with the IntelliJ IDE
How to add graphics to your Spark program
2
Just Enough Linear Algebra for Machine Learning with Spark
Just Enough Linear Algebra for Machine Learning with Spark
Introduction
Package imports and initial setup for vectors and matrices
Creating DenseVector and setup with Spark 2.0
Creating SparseVector and setup with Spark
Creating dense matrix and setup with Spark 2.0
Using sparse local matrices with Spark 2.0
Performing vector arithmetic using Spark 2.0
Performing matrix arithmetic using Spark 2.0
Exploring RowMatrix in Spark 2.0
Exploring Distributed IndexedRowMatrix in Spark 2.0
Exploring distributed CoordinateMatrix in Spark 2.0
Exploring distributed BlockMatrix in Spark 2.0
3
Spark's Three Data Musketeers for Machine Learning - Perfect Together
Spark's Three Data Musketeers for Machine Learning - Perfect Together
Introduction
Creating RDDs with Spark 2.0 using internal data sources
Creating RDDs with Spark 2.0 using external data sources
Transforming RDDs with Spark 2.0 using the filter() API
Transforming RDDs with the super useful flatMap() API
Transforming RDDs with set operation APIs
RDD transformation/aggregation with groupBy() and reduceByKey()
Transforming RDDs with the zip() API
Join transformation with paired key-value RDDs
Reduce and grouping transformation with paired key-value RDDs
Creating DataFrames from Scala data structures
Operating on DataFrames programmatically without SQL
Loading DataFrames and setup from an external source
Using DataFrames with standard SQL language - SparkSQL
Working with the Dataset API using a Scala Sequence
Creating and using Datasets from RDDs and back again
Working with JSON using the Dataset API and SQL together
Functional programming with the Dataset API using domain objects
4
Common Recipes for Implementing a Robust Machine Learning System
Common Recipes for Implementing a Robust Machine Learning System
Introduction
Spark's basic statistical API to help you build your own algorithms
ML pipelines for real-life machine learning applications
Normalizing data with Spark
Splitting data for training and testing
Common operations with the new Dataset API
Creating and using RDD versus DataFrame versus Dataset from a text file in Spark 2.0
LabeledPoint data structure for Spark ML
Getting access to Spark cluster in Spark 2.0
Getting access to Spark cluster pre-Spark 2.0
Getting access to SparkContext vis-a-vis SparkSession object in Spark 2.0
New model export and PMML markup in Spark 2.0
Regression model evaluation using Spark 2.0
Binary classification model evaluation using Spark 2.0
Multiclass classification model evaluation using Spark 2.0
Multilabel classification model evaluation using Spark 2.0
Using the Scala Breeze library to do graphics in Spark 2.0
5
Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I
Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I
Introduction
Fitting a linear regression line to data the old fashioned way
Generalized linear regression in Spark 2.0
Linear regression API with Lasso and L-BFGS in Spark 2.0
Linear regression API with Lasso and 'auto' optimization selection in Spark 2.0
Linear regression API with ridge regression and 'auto' optimization selection in Spark 2.0
Isotonic regression in Apache Spark 2.0
Multilayer perceptron classifier in Apache Spark 2.0
One-vs-Rest classifier (One-vs-All) in Apache Spark 2.0
Survival regression – parametric AFT model in Apache Spark 2.0
6
Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II
Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II
Introduction
Linear regression with SGD optimization in Spark 2.0
Logistic regression with SGD optimization in Spark 2.0
Ridge regression with SGD optimization in Spark 2.0
Lasso regression with SGD optimization in Spark 2.0
Logistic regression with L-BFGS optimization in Spark 2.0
Support Vector Machine (SVM) with Spark 2.0
Naive Bayes machine learning with Spark 2.0 MLlib
Exploring ML pipelines and DataFrames using logistic regression in Spark 2.0
7
Recommendation Engine that Scales with Spark
Recommendation Engine that Scales with Spark
Introduction
Setting up the required data for a scalable recommendation engine in Spark 2.0
Exploring the movies data details for the recommendation system in Spark 2.0
Exploring the ratings data details for the recommendation system in Spark 2.0
Building a scalable recommendation engine using collaborative filtering in Spark 2.0
8
Unsupervised Clustering with Apache Spark 2.0
Unsupervised Clustering with Apache Spark 2.0
Introduction
Building a KMeans classifying system in Spark 2.0
Bisecting KMeans, the new kid on the block in Spark 2.0
Using Gaussian Mixture and Expectation Maximization (EM) in Spark to classify data
Classifying the vertices of a graph using Power Iteration Clustering (PIC) in Spark 2.0
Latent Dirichlet Allocation (LDA) to classify documents and text into topics
Streaming KMeans to classify data in near real-time
9
Optimization - Going Down the Hill with Gradient Descent
Optimization - Going Down the Hill with Gradient Descent
Introduction
Optimizing a quadratic cost function and finding the minima using just math to gain insight
Coding a quadratic cost function optimization using Gradient Descent (GD) from scratch
Coding Gradient Descent optimization to solve Linear Regression from scratch
Normal equations as an alternative for solving Linear Regression in Spark 2.0
10
Building Machine Learning Systems with Decision Tree and Ensemble Models
Building Machine Learning Systems with Decision Tree and Ensemble Models
Introduction
Getting and preparing real-world medical data for exploring Decision Trees and Ensemble models in Spark 2.0
Building a classification system with Decision Trees in Spark 2.0
Solving Regression problems with Decision Trees in Spark 2.0
Building a classification system with Random Forest Trees in Spark 2.0
Solving regression problems with Random Forest Trees in Spark 2.0
Building a classification system with Gradient Boosted Trees (GBT) in Spark 2.0
Solving regression problems with Gradient Boosted Trees (GBT) in Spark 2.0
11
Curse of High-Dimensionality in Big Data
Curse of High-Dimensionality in Big Data
Introduction
Two methods of ingesting and preparing a CSV file for processing in Spark
Singular Value Decomposition (SVD) to reduce high-dimensionality in Spark
Principal Component Analysis (PCA) to pick the most effective latent factor for machine learning in Spark
12
Implementing Text Analytics with Spark 2.0 ML Library
Implementing Text Analytics with Spark 2.0 ML Library
Introduction
Doing term frequency with Spark - everything that counts
Displaying similar words with Spark using Word2Vec
Downloading a complete dump of Wikipedia for a real-life Spark ML project
Using Latent Semantic Analysis for text analytics with Spark 2.0
Topic modeling with Latent Dirichlet allocation in Spark 2.0
13
Spark Streaming and Machine Learning Library
Spark Streaming and Machine Learning Library
Introduction
Structured streaming for near real-time machine learning
Streaming DataFrames for real-time machine learning
Streaming Datasets for real-time machine learning
Streaming data and debugging with queueStream
Downloading and understanding the famous Iris data for unsupervised classification
Streaming KMeans for a real-time on-line classifier
Downloading wine quality data for streaming regression
Streaming linear regression for a real-time regression
Downloading Pima Diabetes data for supervised classification
Streaming logistic regression for an on-line classifier

About the Authors

Siamak Amirghodsi (Sammy) is a world-class senior technology executive leader with an entrepreneurial track record of overseeing big data strategies, cloud transformation, quantitative risk management, advanced analytics, large-scale regulatory data platforming, enterprise architecture, technology road mapping, multi-project execution, and organizational streamlining in Fortune 20 environments in a global setting.

Siamak is a hands-on big data, cloud, machine learning, and AI expert, and is currently overseeing the large-scale cloud data platforming and advanced risk analytics build out for a tier-1 financial institution in the United States. Siamak's interests include building advanced technical teams, executive management, Spark, Hadoop, big data analytics, AI, deep learning nets, TensorFlow, cognitive models, swarm algorithms, real-time streaming systems, quantum computing, financial risk management, trading signal discovery, econometrics, long-term financial cycles, IoT, blockchain, probabilistic graphical models, cryptography, and NLP.

Siamak is fully certified on Cloudera's big data platform and follows Apache Spark, TensorFlow, Hadoop, Hive, Pig, Zookeeper, Amazon AWS, Cassandra, HBase, Neo4j, MongoDB, and GPU architecture, while being fully grounded in the traditional IBM/Oracle/Microsoft technology stack for business continuity and integration.

Siamak has a PMP designation. He holds an advanced degree in computer science and an MBA from the University of Chicago (ChicagoBooth), with emphasis on strategic management, quantitative finance, and econometrics.

 

 

Meenakshi Rajendran is a hands-on big data analytics and data governance manager with expertise in large-scale data platforming and machine learning program execution on a global scale. She is experienced in the end-to-end delivery of data analytics and data science products for leading financial institutions. Meenakshi holds a master's degree in business administration and is a certified PMP with over 13 years of experience in global software delivery environments. She not only understands the underpinnings of big data and data science technology but also has a solid understanding of the human side of the equation as well.

Meenakshi’s favorite languages are Python, R, Julia, and Scala. Her areas of research and interest are Apache Spark, cloud, regulatory data governance, machine learning, Cassandra, and managing global data teams at scale. In her free time, she dabbles in software engineering management literature, cognitive psychology, and chess for relaxation.

Broderick Hall is a hands-on big data analytics expert and holds a master’s degree in computer science with 20 years of experience in designing and developing complex enterprise-wide software applications with real-time and regulatory requirements at a global scale. He has an extensive experience in designing and building real-time financial applications for some of the largest financial institutions and exchanges in USA. He is a deep learning early adopter and is currently working on a large-scale cloud-based data platform with deep learning net augmentation.

Broderick has extensive experience working in healthcare, travel, real estate, and data center management. Broderick also enjoys his role as an adjunct professor, instructing courses in Java programming and object-oriented programming. He is currently focused on delivering real-time big data mission-critical analytics applications in the financial services industry.

Broderick has been actively involved with Hadoop, Spark, Cassandra, TensorFlow, and deep learning since the early days, while actively pursuing machine learning, cloud architecture, data platforms, data science, and practical applications in cognitive sciences. He enjoys programming in Scala, Python, R, Java, and Julia.

 

 

 

Shuen Mei is a big data analytic platforms expert with 15+ years of experience in the financial services industry. He is experienced in designing, building, and executing large-scale, enterprise-distributed financial systems with mission-critical low-latency requirements. He is certified in the Apache Spark, Cloudera Big Data platform, including Developer, Admin, and HBase.

Shuen is also a certified AWS solutions architect with emphasis on peta-byte range real-time data platform systems. Shuen is a skilled software engineer with extensive experience in delivering infrastructure, code, data architecture, and performance tuning solutions in trading and finance for Fortune 100 companies.

Shuen holds a master's degree in MIS from the University of Illinois. He actively follows Spark, TensorFlow, Hadoop, Spark, Cloud Architecture, Apache Flink, Hive, HBase, Cassandra, and related systems. He is passionate about Scala, Python, Java, Julia, cloud computing, machine learning algorithms, and deep learning at scale.

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