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Apache Mahout Essentials

Chapter 1. Introducing Apache Mahout

As you may be already aware, Apache Mahout is an open source library of scalable machine learning algorithms that focuses on clustering, classification, and recommendations.

This chapter will provide an introduction to machine learning and Apache Mahout.

In this chapter, we will cover the following topics:

Machine learning in a nutshell
Machine learning applications
Machine learning libraries
The history of machine learning
Apache Mahout
Setting up Apache Mahout
How Apache Mahout works
From Hadoop MapReduce to Spark
When is it appropriate to use Apache Mahout?

Machine learning in a nutshell

	"Machine learning is the most exciting field of all the computer sciences. Sometimes I actually think that machine learning is not only the most exciting thing in computer science, but also the most exciting thing in all of human endeavor."
	--Andrew Ng, Associate Professor at Stanford and Chief Scientist of Baidu

Giving a detailed explanation of machine learning is beyond the scope of this book. For this purpose, there are other excellent resources that I have listed here:

Machine Learning by Andrew Ng at Coursera (https://www.coursera.org/course/ml)
Foundations of Machine Learning (Adaptive Computation and Machine Learning series) by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalker

However, basic machine learning concepts are explained very briefly here, for those who are not familiar with it.

Machine learning is an area of artificial intelligence that focuses on learning from the available data to make predictions on unseen data without explicit programming.

To solve real-world problems using machine learning, we first need to represent the characteristics of the problem domain using features.

Features

A feature is a distinct, measurable, heuristic property of the item of interest being perceived. We need to consider the features that have the greatest potential in discriminating between different categories.

Supervised learning versus unsupervised learning

Let's explain the difference between supervised learning and unsupervised learning using a simple example of pebbles:

Supervised learning: Take a collection of mixed pebbles, as given in the preceding figure, and categorize (label) them as small, medium, and large pebbles. Examples of supervised learning are regression and classification.
Unsupervised learning: Here, just group them based on similar sizes but don't label them. An example of unsupervised learning is clustering.

For a machine to perform learning tasks, it requires features such as the diameter and weight of each pebble.

This book will cover how to implement the following machine learning techniques using Apache Mahout:

Clustering
Classification and regression
Recommendations

Machine learning applications

Do you know that machine learning has a significant impact in real-life day-to-day applications? World's popular organizations, such as Google, Facebook, Yahoo!, and Amazon, use machine learning algorithms in their applications.

Information retrieval

Information retrieval is an area where machine learning is vastly applied in the industry. Some examples include Google News, Google target advertisements, and Amazon product recommendations.

Google News uses machine learning to categorize large volumes of online news articlesL:

The relevance of Google target advertisements can be improved by using machine learning:

Amazon as well as most of the e-business websites use machine learning to understand which products will interest the users:

Even though information retrieval is the area that has commercialized most of the machine learning applications, machine learning can be applied in various other areas, such as business and health care.

Business

Machine learning is applied to solve different business problems, such as market segmentation, business analytics, risk classification, and stock market predictions.

A few of them are explained here.

Market segmentation (clustering)

In market segmentation, clustering techniques can be used to identify the homogeneous subsets of consumers, as shown in the following figure:

Take an example of a Fast-Moving Consumer Goods (FMCG) company that introduces a shampoo for personal use. They can use clustering to identify the different market segments, by considering features such as the number of people who have hair fall, colored hair, dry hair, and normal hair. Then, they can decide on the types of shampoo required for different market segments, which will maximize the profit.

Stock market predictions (regression)

Regression techniques can be used to predict future trends in stocks by considering features such as closing prices and foreign currency rates.

Health care

Machine learning is heavily used in medical image processing in the health care sector. Using a mammogram for cancer tissue detection is one example of this.

Using a mammogram for cancer tissue detection

Classification techniques can be used for the early detection of breast cancers by analyzing the mammograms with image processing, as shown in the following figure, which is a difficult task for humans due to irregular pathological structures and noise.

Machine learning libraries

Machine learning libraries can be categorized using different criteria, which are explained in the sections that follow.

Open source or commercial

Free and open source libraries are cost-effective solutions, and most of them provide a framework that allows you to implement new algorithms on your own. However, support for these libraries is not as good as the support available for proprietary libraries. However, some open source libraries have very active mailing lists to address this issue.

Apache Mahout, OpenCV, MLib, and Mallet are some open source libraries.

MATLAB is a commercial numerical environment that contains a machine learning library.

Scalability

Machine learning algorithms are resource-intensive (CPU, memory, and storage) operations. Also, most of the time, they are applied on large volumes of datasets. So, decentralization (for example, data and algorithms), distribution, and replication techniques are used to scale out a system:

Apache Mahout (data distributed over clusters and parallel algorithms)
Spark MLib (distributed memory-based Spark architecture)
MLPACK (low memory or CPU requirements due to the use of C++)
GraphLab (multicore parallelism)

Languages used

Most of the machine learning libraries are implemented using languages such as Java, C#, C++, Python, and Scala.

Algorithm support

Machine learning libraries, such as R and Weka, have many machine learning algorithms implemented. However, they are not scalable. So, when it comes to scalable machine learning libraries, Apache Mahout has better algorithm support than Spark MLib at the moment, as Spark MLib is relatively young.

Batch processing versus stream processing

Stream processing mechanisms, for example, Jubatus and Samoa, update a model instantaneously just after receiving data using incremental learning.

In batch processing, data is collected over a period of time and then processed together. In the context of machine learning, the model is updated after collecting data for a period of time. The batch processing mechanism (for example, Apache Mahout) is mostly suitable for processing large volumes of data.

LIBSVM implements support vector machines and it is specialized for that purpose.

A comparison of some of the popular machine learning libraries is given in the following table Table 1: Comparison between popular machine learning libraries:

Machine learning library	Open source or commercial	Scalable?	Language used	Algorithm support
MATLAB	Commercial	No	Mostly C	High
R packages	Open source	No	R	High
Weka	Open source	No	Java	High
Sci-Kit Learn	Open source	No	Python
Apache Mahout	Open source	Yes	Java	Medium
Spark MLib	Open source	Yes	Scala	Low
Samoa	Open source	Yes	Java

Apache Mahout

In this section, we will have a quick look at Apache Mahout.

Do you know how Mahout got its name?

As you can see in the logo, a mahout is a person who drives an elephant. Hadoop's logo is an elephant. So, this is an indicator that Mahout's goal is to use Hadoop in the right manner.

The following are the features of Mahout:

It is a project of the Apache software foundation
It is a scalable machine learning library
- The MapReduce implementation scales linearly with the data
- Fast sequential algorithms (the runtime does not depend on the size of the dataset)
It mainly contains clustering, classification, and recommendation (collaborative filtering) algorithms
Here, machine learning algorithms can be executed in sequential (in-memory mode) or distributed mode (MapReduce is enabled)
Most of the algorithms are implemented using the MapReduce paradigm
It runs on top of the Hadoop framework for scaling
Data is stored in HDFS (data storage) or in memory
It is a Java library (no user interface!)
The latest released version is 0.9, and 1.0 is coming soon
It is not a domain-specific but a general purpose library

Note

For those of you who are curious! What are the problems that Mahout is trying to solve? The following problems that Mahout is trying to solve:

The amount of available data is growing drastically.

The computer hardware market is geared toward providing better performance in computers. Machine learning algorithms are computationally expensive algorithms. However, there was no framework sufficient to harness the power of hardware (multicore computers) to gain better performance.

The need for a parallel programming framework to speed up machine learning algorithms.

Mahout is a general parallelization for machine learning algorithms (the parallelization method is not algorithm-specific).

No specialized optimizations are required to improve the performance of each algorithm; you just need to add some more cores.

Linear speed up with number of cores.

Each algorithm, such as Naïve Bayes, K-Means, and Expectation-maximization, is expressed in the summation form. (I will explain this in detail in future chapters.)

For more information, please read Map-Reduce for Machine Learning on Multicore, which can be found at http://www.cs.stanford.edu/people/ang/papers/nips06-mapreducemulticore.pdf.

Setting up Apache Mahout

Download the latest release of Mahout from https://mahout.apache.org/general/downloads.html.

If you are referencing Mahout as a Maven project, add the following dependency in the pom.xml file:

<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-core</artifactId>
  <version>${mahout.version}</version>
</dependency>

If required, add the following Maven dependencies as well:

<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-math</artifactId>
  <version>${mahout.version}</version>
</dependency>
<dependency>
  <groupId>org.apache.mahout</groupId>
  <artifactId>mahout-integration</artifactId>
  <version>${mahout.version}</version>
</dependency>

Tip

Downloading the example code

You can download the example code files from your account at http://www.packtpub.com for all the Packt Publishing books you have purchased. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

More details on setting up a Maven project can be found at http://maven.apache.org/.

Follow the instructions given at https://mahout.apache.org/developers/buildingmahout.html to build Mahout from the source.

The Mahout command-line launcher is located at bin/mahout.

From Hadoop MapReduce to Spark

Let's take a look at the journey from MapReduce to Spark.

Problems with Hadoop MapReduce

Even though MapReduce provides a suitable programming model for batch data processing, it does not perform well with real-time data processing. When it comes to iterative machine learning algorithms, it is necessary to carry information across iterations. Moreover, an intermediate outcome needs to be persisted during each iteration. Therefore, it is necessary to store and retrieve temporary data from the Hadoop Distributed File System (HDFS) very frequently, which incurs significant performance degradation.

Machine learning algorithms that can be written in a certain form of summation (algorithms that fit in the statistical query model) can be implemented in the MapReduce programming model. However, some of the machine learning algorithms are hard to implement by adhering to the MapReduce programming paradigm. MapReduce cannot be applied if there are any computational dependencies between the data.

Therefore, this constrained programming model is a barrier for Apache Mahout as it can limit the number of supported distributed algorithms.

In-memory data processing with Spark and H2O

Apache Spark is a large-scale scalable data processing framework, which claims to be 100 times faster than Hadoop MapReduce when in memory and 10 times faster in disk, has a distributed memory-based architecture. H2O is an open source, parallel processing engine for machine learning by 0xdata.

As a solution to the problems of the Hadoop MapReduce approach mentioned previously, Apache Mahout is working on integrating Apache Spark and H2O as the backend integration (with the Mahout Math library).

Why is Mahout shifting from Hadoop MapReduce to Spark?

With Spark, there can be better support for iterative machine learning algorithms using the in-memory approach. In-memory applications are self-optimizing. An algebraic expression optimizer is used for distributed linear algebra. One significant example is the Distributed Row Matrix (DRM), which is a huge matrix partitioned by rows.

Further, programming with Spark is easier than programming with MapReduce because Spark decouples the machine learning logic from the distributed backend. Accordingly, the distribution is hidden from the machine learning API users. This can be used like R or MATLAB.

What you will learn

Get started with the fundamentals of Big Data, batch, and realtime data processing with an introduction to Mahout and its applications

Understand the key machine learning concepts behind algorithms in Apache Mahout

Apply machine learning algorithms provided by Apache Mahout in realworld practical scenarios

Implement and evaluate widelyused clustering, classification, and recommendation algorithms using Apache Mahout

Discover tips and tricks to improve the accuracy and performance of your results

Set up Apache Mahout in a production environment with Apache Hadoop

Glance at the Spark DSL advancements in Apache Mahout 1.0

Provide dynamic and interactive data visualizations for Apache Mahout

Build a recommendation engine for realtime use cases and use userbased and itembased recommendation algorithms

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Thimira Amaratunga Aug 17, 2015

This book provides a great starting point for everyone wanting to get in to Apache Mahout and interested in machine learning alike. I really like the way the author has explained many of the machine learning concepts without over-complicating them, which makes this a good book for any machine learning enthusiast. The step-by-step explanations make anyone try it out hands on to learn.

Amazon Verified review

Client d'Amazon Sep 08, 2016

Medium book! Not very detailed!

Pablo Torre Rodriguez Jul 23, 2015

I purchased this book and I think is a good book for those guys want to start to learn about Apache Mahout. This book provides some interesting sample code about Clustering and Recommendations. But if you want to specialize in Apache Mahout you should read Mahout in Action. For me is the best book that you can purchase about Mahout.

Apache Mahout Essentials: Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

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Supervised learning versus unsupervised learning

Market segmentation (clustering)

Stock market predictions (regression)

Using a mammogram for cancer tissue detection

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