Practical Machine Learning with H2O

Preface

It feels like machine learning has finally come of age. It has been a long childhood, stretching back to the 1950s and the first program to learn from experience (playing checkers), as well as the first neural networks. We’ve been told so many times by AI researchers that the breakthrough is “just around the corner” that we long ago stopped listening. But maybe they were on the right track all along, maybe an idea just needs one more order of magnitude of processing power, or a slight algorithmic tweak, to go from being pathetic and pointless to productive and profitable.

In the early ’90s, neural nets were being hailed as the new AI breakthrough. I did some experiments applying them to computer go, but they were truly awful when compared to the (still quite mediocre) results I could get using a mix of domain-specific knowledge engineering, and heavily pruned tree searches. And the ability to scale looked poor, too. When, 20 years later, I heard talk of this new and shiny deep learning thing that was giving impressive results in computer go, I was confused how this was different from the neural nets I’d rejected all those years earlier. “Not that much” was the answer; sometimes you just need more processing power (five or six orders of magnitude in this case) for an algorithm to bear fruit.

H2O is software for machine learning and data analysis. Wanting to see what other magic deep learning could perform was what personally led me to H2O (though it does more than that: trees, linear models, unsupervised learning, etc.), and I was immediately impressed. It ticks all the boxes:

• Open source (the liberal Apache license)

• Easy to use

• Scalable to big data

• Well-documented and commercially supported

• On its third version (i.e., a mature architecture)

• Wide range of OS/language support

With the high-quality team that H2O.ai (the company behind H2O) has put together, it is only going to get better. There is the attitude of not just “How do we get this to work?” but “How do we get this to work efficiently at big data scale?” permeating the whole development.

If machine learning has come of age, H2O looks to be not just an economical family car for it, but simultaneously the large load delivery truck for it. Stretching my vehicle analogy a bit further, this book will show you not just what the dashboard controls do, but also the best way to use them to get from A to B. It will be as practical as possible, with only the bare minimum explanation of the maths or theory behind the learning algorithms.

Of course H2O is not perfect; here are a few issues I’ve noticed people mutter about. There is no GPU support (which could make deep learning, in particular, quicker).¹ The cluster support is all ’bout that bass (big data), no treble (complex but relatively small data), so for the latter you may be limited to needing a single, fast, machine with lots of cores. Also no high availability (HA) for clusters. H2O compiles to Java; it is well-optimized and the H2O algorithms are known for their speed but, theoretically at least, carefully optimized C++ could be quicker. There is no SVM algorithm. Finally, it tries to support numerous platforms, so each has some rough edges, and development is sometimes slowed by trying to keep them all in sync.

In other words, and wringing the last bit of life out of my car analogy: a Formula 1 car might beat it on the straights, and it isn’t yet available in yellow.

Who Uses It and Why?

A number of well-known companies are using H2O for their big data processing, and the website claims that over 5000 organizations currently use it. The company behind it, H2O.ai, has over 80 staff, more than half of which are developers.

But those are stats to impress your boss, not a no-nonsense developer. For R and Python developers, who already feel they have all the machine learning libraries they need, the primary things H2O brings are ease of use and efficient scalability to data sets too large to fit in the memory of your largest machine. For SparkML users, who feel they already have that, H2O algorithms are fewer in number but apparently significantly quicker. As a bonus, the intelligent defaults mean your code is very compact and clear to read: you can literally get a well-tuned, state-of-the-art, deep learning model as a one-liner. One of the goals of this book was to show you how to tune the models, but as we will see, sometimes I’ve just had to give up and say I can’t beat the defaults.

About You

To bring this book in at under a thousand pages, I’ve taken some liberties. I am assuming you know either R or Python. Advanced language features are not used, so competence in any programming language should be enough to follow along, but the examples throughout the book are only in one of those two languages. Python users would benefit from being familiar with pandas, not least because it will make all your data science easier.

I’m also assuming a bit of mental flexibility: to save repeating every example twice, I’m hoping R users can grasp what is going on in a Python example, and Python users can grasp an R example. These slides on Python for R users are a good start (for R users too).

Some experience with manipulating data is assumed, even if just using spreadsheet software or SQL tables. And I assume you have a fair idea of what machine learning and AI are, and how they are being used more and more in the infrastructure that runs our society. Maybe you are reading this book because you want to be part of that and make sure the transformations to come are done ethically and for the good of everyone, whatever their race, sex, nationality, or beliefs. If so, I salute you.

I am also assuming you know a bit of statistics. Nothing too scary—this book takes the “Practical” in the title seriously, and the theory behind the machine-learning algorithms is kept to the minimum needed to know how to tune them (as opposed to being able to implement them from scratch). Use Wikipedia or a search engine for when you crave more. But you should know your mean from your median from your mode, and know what a standard deviation and the normal distribution are.

But more than that, I am hoping you know that statistics can mislead, and machine learning can overfit. That you appreciate that when someone says an experiment is significant to p = 0.05 it means that out of every 20 such experiments you read about, probably one of them is wrong. A good moment to enjoy Significant, on xkcd.

This might also be a good time to mention “my machine,” which I sometimes reference for timings. It is a mid-level notebook, a couple of years old, 8GB of memory, four real cores, eight hyper-threads. This is capable of running everything in the book; in fact 4GB of system memory should be enough. However, for some of the grid searches (described in Chapter 5) I “cheated” and started up a cluster in the cloud (covered, albeit briefly, in “Clusters” in Chapter 10). I did this just out of practicality: not wanting to wait 24 hours for an experiment to finish before I can write about it.

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Acknowledgments

Firstly, a big thanks to the technical reviewers: it is a cliche to say the book is better because of you, but it is certainly true. Another cliche is that the remaining errors are mine, but that is true too. So, to Katharine Jarmul, Yulin Zhuang, Hugo Mathien, Erin LeDell, Tom Kraljevic: thanks, and I’m sorry if a change you suggested didn’t get in, or if a joke you scribbled out is still in here. In addition to Erin and Tom, a host of other people at H2O.ai were super-helpful in answering my questions, so a big thank-you to Arno Candel, Tomas Nykodym, Michal Kurka, Navdeep Gill, SriSatish Ambati, Lauren DiPerna, and anyone else I’ve overlooked. (Sorry!)

Thanks to Nicole Tache for being the editor on the first half of book production, and to Debbie Hardin for taking over when Nicole decided the only way to escape this project was to have a baby. A bit extreme. Thanks to both of you for staying calm when I got so absorbed in building models for the book that I forgot about things like deadlines.

Thanks to my family for quietly tolerating the very long hours I’ve been putting into this book.

Finally, thanks to everyone else: the people who answer questions on StackOverflow, post blog articles, post video tutorials, write books, keep Wikipedia accurate. They worked around the clock to plug most of the holes in my knowledge. Which brings me full circle: don’t hesitate to let me know about the remaining errors in the book. Or simply how anything here can be done better.

¹ Deep Water is a new H2O project, in development, to allow interaction with other deep learning libraries, and so it will soon support GPUs that way.