scala-glm: Regression modelling in Scala


As discussed in the previous post, I’ve recently constructed and delivered a short course on statistical computing with Scala. Much of the course is concerned with writing statistical algorithms in Scala, typically making use of the scientific and numerical computing library, Breeze. Breeze has all of the essential tools necessary for building statistical algorithms, but doesn’t contain any higher level modelling functionality. As part of the course, I walked through how to build a small library for regression modelling on top of Breeze, including all of the usual regression diagnostics (such as standard errors, t-statistics, p-values, F-statistics, etc.). While preparing the course materials it occurred to me that it would be useful to package and document this code properly for general use. In advance of the course I packaged the code up into a bare-bones library, but since then I’ve fleshed it out, tidied it up and documented it properly, so it’s now ready for people to use.

The library covers PCA, linear regression modelling and simple one-parameter GLMs (including logistic and Poisson regression). The underlying algorithms are fairly efficient and numerically stable (eg. linear regression uses the QR decomposition of the model matrix, and the GLM fitting uses QR within each IRLS step), though they are optimised more for clarity than speed. The library also includes a few utility functions and procedures, including a pairs plot (scatter-plot matrix).

A linear regression example

Plenty of documentation is available from the scala-glm github repo which I won’t repeat here. But to give a rough idea of how things work, I’ll run through an interactive session for the linear regression example.

First, download a dataset from the UCI ML Repository to disk for subsequent analysis (caching the file on disk is good practice, as it avoids unnecessary load on the UCI server, and allows running the code off-line):

import scalaglm._
import breeze.linalg._

val url = ""
val fileName = "self-noise.csv"

// download the file to disk if it hasn't been already
val file = new
if (!file.exists) {
  val s = new
  val data =
  data.foreach(l => s.write(l.trim.
    split('\t').filter(_ != "").
    mkString("", ",", "\n")))

Once we have a CSV file on disk, we can load it up and look at it.

val mat = csvread(new
// mat: breeze.linalg.DenseMatrix[Double] =
// 800.0    0.0  0.3048  71.3  0.00266337  126.201
// 1000.0   0.0  0.3048  71.3  0.00266337  125.201
// 1250.0   0.0  0.3048  71.3  0.00266337  125.951
// ...
println("Dim: " + mat.rows + " " + mat.cols)
// Dim: 1503 6
val figp = Utils.pairs(mat, List("Freq", "Angle", "Chord", "Velo", "Thick", "Sound"))
// figp: breeze.plot.Figure = breeze.plot.Figure@37718125

We can then regress the response in the final column on the other variables.

val y = mat(::, 5) // response is the final column
// y: DenseVector[Double] = DenseVector(126.201, 125.201, ...
val X = mat(::, 0 to 4)
// X: breeze.linalg.DenseMatrix[Double] =
// 800.0    0.0  0.3048  71.3  0.00266337
// 1000.0   0.0  0.3048  71.3  0.00266337
// 1250.0   0.0  0.3048  71.3  0.00266337
// ...
val mod = Lm(y, X, List("Freq", "Angle", "Chord", "Velo", "Thick"))
// mod: scalaglm.Lm =
// Lm(DenseVector(126.201, 125.201, ...
// Estimate	 S.E.	 t-stat	p-value		Variable
// ---------------------------------------------------------
// 132.8338	 0.545	243.866	0.0000 *	(Intercept)
//  -0.0013	 0.000	-30.452	0.0000 *	Freq
//  -0.4219	 0.039	-10.847	0.0000 *	Angle
// -35.6880	 1.630	-21.889	0.0000 *	Chord
//   0.0999	 0.008	12.279	0.0000 *	Velo
// -147.3005	15.015	-9.810	0.0000 *	Thick
// Residual standard error:   4.8089 on 1497 degrees of freedom
// Multiple R-squared: 0.5157, Adjusted R-squared: 0.5141
// F-statistic: 318.8243 on 5 and 1497 DF, p-value: 0.00000
val fig = mod.plots
// fig: breeze.plot.Figure = breeze.plot.Figure@60d7ebb0

There is a .predict method for generating point predictions (and standard errors) given a new model matrix, and fitting GLMs is very similar – these things are covered in the quickstart guide for the library.


scala-glm is a small Scala library built on top of the Breeze numerical library which enables simple and convenient regression modelling in Scala. It is reasonably well documented and usable in its current form, but I intend to gradually add additional features according to demand as time permits.

Scala for Machine Learning [book review]

Full disclosure: I received a free electronic version of this book from the publisher for the purposes of review.

There is clearly a market for a good book about using Scala for statistical computing, machine learning and data science. So when the publisher of “Scala for Machine Learning” offered me a copy for review purposes, I eagerly accepted. Three months later, I have eventually forced myself to read through the whole book, but I was very disappointed. It is important to be clear that I’m not just disappointed because I personally didn’t get much from the book – I am not really the target audience. I am disappointed because I struggle to envisage any audience that will benefit greatly from reading this book. There are several potential audiences for a book with this title: eg. people with little knowledge of Scala or machine learning (ML), people with knowledge of Scala but not ML, people with knowledge of ML but not Scala, and people with knowledge of both. I think there is scope for a book targeting any of those audiences. Personally, I fall in the latter category. The book author claimed to be aiming primarily at those who know Scala but not ML. This is sensible in that the book assumes a good working knowledge of Scala, and uses advanced features of the Scala language without any explanation: this book is certainly not appropriate for people hoping to learn about Scala in the context of ML. However, it is also a problem, as this would probably be the worst book I have ever encountered for learning about ML from scratch, and there are a lot of poor books about ML! The book just picks ML algorithms out of thin air without any proper explanation or justification, and blindly applies them to tedious financial data sets irrespective of whether or not it would be in any way appropriate to do so. It presents ML as an incoherent “bag of tricks” to be used indiscriminately on any data of the correct “shape”. It is by no means the only ML book to take such an approach, but there are many much better books which don’t. The author also claims that the book will be useful to people who know ML but not Scala, but as previously explained, I do not think that this is the case (eg. monadic traits appear on the fifth page, without proper explanation, and containing typos). I think that the only audience that could potentially benefit from this book would be people who know some Scala and some ML and want to see some practical examples of real world implementations of ML algorithms in Scala. I think those people will also be disappointed, for reasons outlined below.

The first problem with the book is that it is just full of errors and typos. It doesn’t really matter to me that essentially all of the equations in the first chapter are wrong – I already know the difference between an expectation and a sample mean, and know Bayes theorem – so I can just see that they are wrong, correct them, and move on. But for the intended audience it would be a complete nightmare. I wonder about the quality of copy-editing and technical review that this book received – it is really not of “publishable” quality. All of the descriptions of statistical/ML methods and algorithms are incredibly superficial, and usually contain factual errors or typos. One should not attempt to learn ML by reading this book. So the only hope for this book is that the Scala implementations of ML algorithms are useful and insightful. Again, I was disappointed.

For reasons that are not adequately explained or justified, the author decides to use a combination of plain Scala interfaced to legacy Java libraries (especially Apache Commons Math) for all of the example implementations. In addition, the author is curiously obsessed with an F# style pipe operator, which doesn’t seem to bring much practical benefit. Consequently, all of the code looks like a strange and rather inelegant combination of Java, Scala, C++, and F#, with a hint of Haskell, and really doesn’t look like clean idiomatic Scala code at all. For me this was the biggest disappointment of all – I really wouldn’t want any of this code in my own Scala code base (though the licensing restrictions on the code probably forbid this, anyway). It is a real shame that Scala libraries such as Breeze were not used for all of the examples – this would have led to much cleaner and more idiomatic Scala code, which could have really taken proper advantage of the functional power of the Scala language. As it is, advanced Scala features were used without much visible pay-off. Reading this book one could easily get the (incorrect) impression that Scala is an unnecessarily complex language which doesn’t offer much advantage over Java for implementing ML algorithms.

On the positive side, the book consists of nearly 500 pages of text, covering a wide range of ML algorithms and examples, and has a zip file of associated code containing the implementation and examples, which builds using sbt. If anyone is interested in seeing examples of ML algorithms implemented in Scala using Java rather than Scala libraries together with a F# pipe operator, then there is definitely something of substance here of interest.


It should be clear from the above review that I think there is still a gap in the market for a good book about using Scala for statistical computing, machine learning and data science. Hopefully someone will fill this gap soon. In the meantime it is necessary to learn about Scala and ML separately, and to put the ideas together yourself. This isn’t so difficult, as there are many good resources and code repositories to help. For learning about ML, I would recommend starting off with ISLR, which uses R for the examples (but if you work in data science, you need to know R anyway). Once the basic concepts are understood, one can move on to a serious text, such as Machine Learning (which has associated Matlab code). Converting algorithms from R or Matlab to Scala (plus Breeze) is generally very straightforward, if you know Scala. For learning Scala, there are many on-line resources. If you want books, I recommend Functional Programming in Scala and Programming in Scala, 2e. Once you know about Scala, learn about scientific computing using Scala by figuring out Breeze. At some point you will probably also want to know about Spark, and there are now books on this becoming available – I’ve just got a copy of Learning Spark, which looks OK.