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Spark Cookbook

By : Rishi Yadav
Book Image

Spark Cookbook

By: Rishi Yadav

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Spark Cookbook
Rishi Yadav
Jul, 2015 | 226 pages
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Table of Contents (19 chapters)
Spark Cookbook
Spark Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Apache Spark
Getting Started with Apache Spark
Introduction
Installing Spark from binaries
Building the Spark source code with Maven
Launching Spark on Amazon EC2
Deploying on a cluster in standalone mode
Deploying on a cluster with Mesos
Deploying on a cluster with YARN
Using Tachyon as an off-heap storage layer
2
Developing Applications with Spark
Developing Applications with Spark
Introduction
Exploring the Spark shell
Developing Spark applications in Eclipse with Maven
Developing Spark applications in Eclipse with SBT
Developing a Spark application in IntelliJ IDEA with Maven
Developing a Spark application in IntelliJ IDEA with SBT
3
External Data Sources
External Data Sources
Introduction
Loading data from the local filesystem
Loading data from HDFS
Loading data from HDFS using a custom InputFormat
Loading data from Amazon S3
Loading data from Apache Cassandra
Loading data from relational databases
4
Spark SQL
Spark SQL
Introduction
Understanding the Catalyst optimizer
Creating HiveContext
Inferring schema using case classes
Programmatically specifying the schema
Loading and saving data using the Parquet format
Loading and saving data using the JSON format
Loading and saving data from relational databases
Loading and saving data from an arbitrary source
5
Spark Streaming
Spark Streaming
Introduction
Word count using Streaming
Streaming Twitter data
Streaming using Kafka
6
Getting Started with Machine Learning Using MLlib
Getting Started with Machine Learning Using MLlib
Introduction
Creating vectors
Creating a labeled point
Creating matrices
Calculating summary statistics
Calculating correlation
Doing hypothesis testing
Creating machine learning pipelines using ML
7
Supervised Learning with MLlib – Regression
Supervised Learning with MLlib – Regression
Introduction
Using linear regression
Understanding cost function
Doing linear regression with lasso
Doing ridge regression
8
Supervised Learning with MLlib – Classification
Supervised Learning with MLlib – Classification
Introduction
Doing classification using logistic regression
Doing binary classification using SVM
Doing classification using decision trees
Doing classification using Random Forests
Doing classification using Gradient Boosted Trees
Doing classification with Naïve Bayes
9
Unsupervised Learning with MLlib
Unsupervised Learning with MLlib
Introduction
Clustering using k-means
Dimensionality reduction with principal component analysis
Dimensionality reduction with singular value decomposition
10
Recommender Systems
Recommender Systems
Introduction
Collaborative filtering using explicit feedback
Collaborative filtering using implicit feedback
11
Graph Processing Using GraphX
Graph Processing Using GraphX
Introduction
Fundamental operations on graphs
Using PageRank
Finding connected components
Performing neighborhood aggregation
12
Optimizations and Performance Tuning
Optimizations and Performance Tuning
Introduction
Optimizing memory
Using compression to improve performance
Using serialization to improve performance
Optimizing garbage collection
Optimizing the level of parallelism
Understanding the future of optimization – project Tungsten
Index
Index

Doing classification using Gradient Boosted Trees

Another ensemble learning algorithm is Gradient Boosted Trees (GBTs). GBTs train one tree at a time, where each new tree improves upon the shortcomings of previously trained trees.

As GBTs train one tree at a time, they can take longer than Random Forest.

Getting ready

We are going to use the same data we used in the previous recipe.

How to do it…

  1. Start the Spark shell:

    $ spark-shell

  2. Perform the required imports:

    scala> import org.apache.spark.mllib.tree.GradientBoostedTrees
scala> import org.apache.spark.mllib.tree.configuration.BoostingStrategy
scala> import org.apache.spark.mllib.util.MLUtils

  3. Load and parse the data:

    scala> val data =
  MLUtils.loadLibSVMFile(sc, "rf_libsvm_data.txt")

  4. Split the data into training and test datasets:

    scala> val splits = data.randomSplit(Array(0.7, 0.3))
scala> val (trainingData, testData) = (splits(0), splits(1))

  5. Create a classification as a boosting strategy and set the number of iterations...

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