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Mastering Scala Machine Learning
Mastering Scala Machine Learning

Mastering Scala Machine Learning: Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop

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Mastering Scala Machine Learning

Chapter 2. Data Pipelines and Modeling

We have looked at basic hands-on tools for exploring the data in the previous chapter, thus we now can delve into more complex topics of statistical model building and optimal control or science-driven tools and problems. I will go ahead and say that we will only touch on some topics in optimal control since this book really is just about ML in Scala and not the theory of data-driven business management, which might be an exciting topic for a book on its own.

In this chapter, I will stay away from specific implementations in Scala and discuss the problem of building a data-driven enterprise at a high level. Later chapters will address how to solve these smaller pieces of the puzzle. A special emphasis will be given to handing uncertainty. Uncertainty usually comes in several favors: first, there can be noise in the information we are provided with. Secondly, the information can be incomplete. The system may have some degree of freedom in filling...

Influence diagrams

While the decision making process can have multiple facets, a book about decision making under uncertainty would be incomplete without mentioning influence diagrams (Influence Diagrams for Team Decision Analysis, Decision Analysis 2 (4): 207–228), which help the analysis and understanding of the decision-making process. The decision may be as mundane as selection of the next news article to show to a user in a personalized environment or a complex one as detecting malware on an enterprise network or selecting the next research project.

Depending on the weather she can try and go on a boat trip. We can represent the decision-making process as a diagram. Let's decide whether to take a river boat tour during her stay in Portland, Oregon:

Influence diagrams

Figure 02-1. A simple vacation influence diagram to represent a simple decision-making process. The diagram contains decision nodes such as Vacation Activity, observable and unobservable information nodes such as Weather Forecast...

Sequential trials and dealing with risk

What if my preferences for making an extra few dollars outweigh the risk of losing the same amount? I will stop on why one's preferences might be asymmetric in a little while in this section, and there is scientific evidence that this asymmetry is ingrained in our minds for evolutionary reasons, but you are right, I have to optimize the expected value of the asymmetric function of the parameterized utility now, as follows:

Sequential trials and dealing with risk

Why would an asymmetric function surface in the analysis? One example is repeated bets or re-investments, also known as the Kelly Criterion problem. Although originally, the Kelly Criterion was developed for a specific case of binary outcome as in a gambling machine and the optimization of the fraction of money to bet in each round (A New Interpretation of Information Rate, Bell System Technical Journal 35 (4): 917–926, 1956), a more generic formulation as an re-investment problem involves a probabilistic distribution...

Exploration and exploitation

The exploration-exploitation trade-off is another problem that has its apparent origin within gambling, even though the real applications range from allocation of funding to research projects to self-driving cars. The traditional formulation is a multi-armed bandit problem, which refers to an imaginary slot machine with one or more arms. Sequential plays of each arm generate i.i.d . returns with unknown probabilities for each arm; the successive plays are independent in the simplified models. The rewards are assumed to be independent across the arms. The goal is to maximize the reward—for example, the amount of money won, and to minimize the learning loss, or the amount spend on the arms with less than optimal winning rate, provided an agreed upon arm selection policy. The obvious trade-off is between the exploration in search of an arm that produces the best return and exploitation of the best-known arm with optimal return:

Exploration and exploitation

The pseudo-regret is then the...

Unknown unknowns

Unknown unknowns have been largely made famous due to a phrase from a response the United States Secretary of Defense, Donald Rumsfeld, gave to a question at a United States Department of Defense (DoD) news briefing on February 12, 2002 about the lack of evidence linking the government of Iraq with the supply of weapons of mass destruction to terrorist groups, and books by Nassim Taleb (The Black Swan: The Impact of the Highly Improbable by Nassim Taleb, Random House, 2007).

Note

Turkey paradox

Arguably, the unknown unknown is better explained by the turkey paradox. Suppose you have a family of turkeys playing in the backyard and enjoying protection and free food. Across the fence, there is another family of turkeys. This all works day after day, and month after month, until Thanksgiving comes—Thanksgiving Day is a national holiday celebrated in Canada and the United States, where it's customary to roast the turkeys in an oven. The turkeys are very likely to...

Basic components of a data-driven system

In short, a data-driven architecture contains the following components—at least all the systems I've seen have them—or can be reduced to these components:

  • Data ingest: We need to collect the data from systems and devices. Most of the systems have logs, or at least an option to write files into a local filesystem. Some can have capabilities to report information to network-based interfaces such as syslog, but the absence of persistence layer usually means potential data loss, if not absence of audit information.
  • Data transformation layer: It was also historically called extract, transform, and load (ETL). Today the data transformation layer can also be used to have real-time processing, where the aggregates are computed on the most recent data. The data transformation layer is also traditionally used to reformat and index the data to be efficiently accessed by a UI component of algorithms down the pipeline.
  • Data analytics and machine...

Influence diagrams


While the decision making process can have multiple facets, a book about decision making under uncertainty would be incomplete without mentioning influence diagrams (Influence Diagrams for Team Decision Analysis, Decision Analysis 2 (4): 207–228), which help the analysis and understanding of the decision-making process. The decision may be as mundane as selection of the next news article to show to a user in a personalized environment or a complex one as detecting malware on an enterprise network or selecting the next research project.

Depending on the weather she can try and go on a boat trip. We can represent the decision-making process as a diagram. Let's decide whether to take a river boat tour during her stay in Portland, Oregon:

Figure 02-1. A simple vacation influence diagram to represent a simple decision-making process. The diagram contains decision nodes such as Vacation Activity, observable and unobservable information nodes such as Weather Forecast and Weather...

Sequential trials and dealing with risk


What if my preferences for making an extra few dollars outweigh the risk of losing the same amount? I will stop on why one's preferences might be asymmetric in a little while in this section, and there is scientific evidence that this asymmetry is ingrained in our minds for evolutionary reasons, but you are right, I have to optimize the expected value of the asymmetric function of the parameterized utility now, as follows:

Why would an asymmetric function surface in the analysis? One example is repeated bets or re-investments, also known as the Kelly Criterion problem. Although originally, the Kelly Criterion was developed for a specific case of binary outcome as in a gambling machine and the optimization of the fraction of money to bet in each round (A New Interpretation of Information Rate, Bell System Technical Journal 35 (4): 917–926, 1956), a more generic formulation as an re-investment problem involves a probabilistic distribution of possible...

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Key benefits

  • This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data
  • Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning
  • Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability

Description

Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala.

Who is this book for?

Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required.

What you will learn

  • Sharpen your functional programming skills in Scala using REPL
  • Apply standard and advanced machine learning techniques using Scala
  • Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data
  • Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data
  • Understand the principles of supervised and unsupervised learning in machine learning
  • Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet
  • Construct reliable and robust data pipelines and manage data in a data-driven enterprise
  • Implement scalable model monitoring and alerts with Scala
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Table of Contents

11 Chapters
1. Exploratory Data Analysis Chevron down icon Chevron up icon
2. Data Pipelines and Modeling Chevron down icon Chevron up icon
3. Working with Spark and MLlib Chevron down icon Chevron up icon
4. Supervised and Unsupervised Learning Chevron down icon Chevron up icon
5. Regression and Classification Chevron down icon Chevron up icon
6. Working with Unstructured Data Chevron down icon Chevron up icon
7. Working with Graph Algorithms Chevron down icon Chevron up icon
8. Integrating Scala with R and Python Chevron down icon Chevron up icon
9. NLP in Scala Chevron down icon Chevron up icon
10. Advanced Model Monitoring Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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