Comonads for scientific and statistical computing in Scala

Introduction

In a previous post I’ve given a brief introduction to monads in Scala, aimed at people interested in scientific and statistical computing. Monads are a concept from category theory which turn out to be exceptionally useful for solving many problems in functional programming. But most categorical concepts have a dual, usually prefixed with “co”, so the dual of a monad is the comonad. Comonads turn out to be especially useful for formulating algorithms from scientific and statistical computing in an elegant way. In this post I’ll illustrate their use in signal processing, image processing, numerical integration of PDEs, and Gibbs sampling (of an Ising model). Comonads enable the extension of a local computation to a global computation, and this pattern crops up all over the place in statistical computing.

Monads and comonads

Simplifying massively, from the viewpoint of a Scala programmer, a monad is a mappable (functor) type class augmented with the methods pure and flatMap:

trait Monad[M[_]] extends Functor[M] {
  def pure[T](v: T): M[T]
  def flatMap[T,S](v: M[T])(f: T => M[S]): M[S]
}

In category theory, the dual of a concept is typically obtained by “reversing the arrows”. Here that means reversing the direction of the methods pure and flatMap to get extract and coflatMap, respectively.

trait Comonad[W[_]] extends Functor[W] {
  def extract[T](v: W[T]): T
  def coflatMap[T,S](v: W[T])(f: W[T] => S): W[S]
}

So, while pure allows you to wrap plain values in a monad, extract allows you to get a value out of a comonad. So you can always get a value out of a comonad (unlike a monad). Similarly, while flatMap allows you to transform a monad using a function returning a monad, coflatMap allows you to transform a comonad using a function which collapses a comonad to a single value. It is coflatMap (sometimes called extend) which can extend a local computation (producing a single value) to the entire comonad. We’ll look at how that works in the context of some familiar examples.

Applying a linear filter to a data stream

One of the simplest examples of a comonad is an infinite stream of data. I’ve discussed streams in a previous post. By focusing on infinite streams we know the stream will never be empty, so there will always be a value that we can extract. Which value does extract give? For a Stream encoded as some kind of lazy list, the only value we actually know is the value at the head of the stream, with subsequent values to be lazily computed as required. So the head of the list is the only reasonable value for extract to return.

Understanding coflatMap is a bit more tricky, but it is coflatMap that provides us with the power to apply a non-trivial statistical computation to the stream. The input is a function which transforms a stream into a value. In our example, that will be a function which computes a weighted average of the first few values and returns that weighted average as the result. But the return type of coflatMap must be a stream of such computations. Following the types, a few minutes thought reveals that the only reasonable thing to do is to return the stream formed by applying the weighted average function to all sub-streams, recursively. So, for a Stream s (of type Stream[T]) and an input function f: W[T] => S, we form a stream whose head is f(s) and whose tail is coflatMap(f) applied to s.tail. Again, since we are working with an infinite stream, we don’t have to worry about whether or not the tail is empty. This gives us our comonadic Stream, and it is exactly what we need for applying a linear filter to the data stream.

In Scala, Cats is a library providing type classes from Category theory, and instances of those type classes for parametrised types in the standard library. In particular, it provides us with comonadic functionality for the standard Scala Stream. Let’s start by defining a stream corresponding to the logistic map.

import cats._
import cats.implicits._

val lam = 3.7
def s = Stream.iterate(0.5)(x => lam*x*(1-x))
s.take(10).toList
// res0: List[Double] = List(0.5, 0.925, 0.25668749999999985,
//  0.7059564011718747, 0.7680532550204203, 0.6591455741499428, ...

Let us now suppose that we want to apply a linear filter to this stream, in order to smooth the values. The idea behind using comonads is that you figure out how to generate one desired value, and let coflatMap take care of applying the same logic to the rest of the structure. So here, we need a function to generate the first filtered value (since extract is focused on the head of the stream). A simple first attempt a function to do this might look like the following.

  def linearFilterS(weights: Stream[Double])(s: Stream[Double]): Double =
    (weights, s).parMapN(_*_).sum

This aligns each weight in parallel with a corresponding value from the stream, and combines them using multiplication. The resulting (hopefully finite length) stream is then summed (with addition). We can test this with

linearFilterS(Stream(0.25,0.5,0.25))(s)
// res1: Double = 0.651671875

and let coflatMap extend this computation to the rest of the stream with something like:

s.coflatMap(linearFilterS(Stream(0.25,0.5,0.25))).take(5).toList
// res2: List[Double] = List(0.651671875, 0.5360828502929686, ...

This is all completely fine, but our linearFilterS function is specific to the Stream comonad, despite the fact that all we’ve used about it in the function is that it is a parallelly composable and foldable. We can make this much more generic as follows:

  def linearFilter[F[_]: Foldable, G[_]](
    weights: F[Double], s: F[Double]
  )(implicit ev: NonEmptyParallel[F, G]): Double =
    (weights, s).parMapN(_*_).fold

This uses some fairly advanced Scala concepts which I don’t want to get into right now (I should also acknowledge that I had trouble getting the syntax right for this, and got help from Fabio Labella (@SystemFw) on the Cats gitter channel). But this version is more generic, and can be used to linearly filter other data structures than Stream. We can use this for regular Streams as follows:

s.coflatMap(s => linearFilter(Stream(0.25,0.5,0.25),s))
// res3: scala.collection.immutable.Stream[Double] = Stream(0.651671875, ?)

But we can apply this new filter to other collections. This could be other, more sophisticated, streams such as provided by FS2, Monix or Akka streams. But it could also be a non-stream collection, such as List:

val sl = s.take(10).toList
sl.coflatMap(sl => linearFilter(List(0.25,0.5,0.25),sl))
// res4: List[Double] = List(0.651671875, 0.5360828502929686, ...

Assuming that we have the Breeze scientific library available, we can plot the raw and smoothed trajectories.

def myFilter(s: Stream[Double]): Double =
  linearFilter(Stream(0.25, 0.5, 0.25),s)
val n = 500
import breeze.plot._
import breeze.linalg._
val fig = Figure(s"The (smoothed) logistic map (lambda=$lam)")
val p0 = fig.subplot(3,1,0)
p0 += plot(linspace(1,n,n),s.take(n))
p0.ylim = (0.0,1.0)
p0.title = s"The logistic map (lambda=$lam)"
val p1 = fig.subplot(3,1,1)
p1 += plot(linspace(1,n,n),s.coflatMap(myFilter).take(n))
p1.ylim = (0.0,1.0)
p1.title = "Smoothed by a simple linear filter"
val p2 = fig.subplot(3,1,2)
p2 += plot(linspace(1,n,n),s.coflatMap(myFilter).coflatMap(myFilter).coflatMap(myFilter).coflatMap(myFilter).coflatMap(myFilter).take(n))
p2.ylim = (0.0,1.0)
p2.title = "Smoothed with 5 applications of the linear filter"
fig.refresh

Image processing and the heat equation

Streaming data is in no way the only context in which a comonadic approach facilitates an elegant approach to scientific and statistical computing. Comonads crop up anywhere where we want to extend a computation that is local to a small part of a data structure to the full data structure. Another commonly cited area of application of comonadic approaches is image processing (I should acknowledge that this section of the post is very much influenced by a blog post on comonadic image processing in Haskell). However, the kinds of operations used in image processing are in many cases very similar to the operations used in finite difference approaches to numerical integration of partial differential equations (PDEs) such as the heat equation, so in this section I will blur (sic) the distinction between the two, and numerically integrate the 2D heat equation in order to Gaussian blur a noisy image.

First we need a simple image type which can have pixels of arbitrary type T (this is very important – all functors must be fully type polymorphic).

  import scala.collection.parallel.immutable.ParVector
  case class Image[T](w: Int, h: Int, data: ParVector[T]) {
    def apply(x: Int, y: Int): T = data(x*h+y)
    def map[S](f: T => S): Image[S] = Image(w, h, data map f)
    def updated(x: Int, y: Int, value: T): Image[T] =
      Image(w,h,data.updated(x*h+y,value))
  }

Here I’ve chosen to back the image with a parallel immutable vector. This wasn’t necessary, but since this type has a map operation which automatically parallelises over multiple cores, any map operations applied to the image will be automatically parallelised. This will ultimately lead to all of our statistical computations being automatically parallelised without us having to think about it.

As it stands, this image isn’t comonadic, since it doesn’t implement extract or coflatMap. Unlike the case of Stream, there isn’t really a uniquely privileged pixel, so it’s not clear what extract should return. For many data structures of this type, we make them comonadic by adding a “cursor” pointing to a “current” element of interest, and use this as the focus for computations applied with coflatMap. This is simplest to explain by example. We can define our “pointed” image type as follows:

  case class PImage[T](x: Int, y: Int, image: Image[T]) {
    def extract: T = image(x, y)
    def map[S](f: T => S): PImage[S] = PImage(x, y, image map f)
    def coflatMap[S](f: PImage[T] => S): PImage[S] = PImage(
      x, y, Image(image.w, image.h,
      (0 until (image.w * image.h)).toVector.par.map(i => {
        val xx = i / image.h
        val yy = i % image.h
        f(PImage(xx, yy, image))
      })))

There is missing a closing brace, as I’m not quite finished. Here x and y represent the location of our cursor, so extract returns the value of the pixel indexed by our cursor. Similarly, coflatMap forms an image where the value of the image at each location is the result of applying the function f to the image which had the cursor set to that location. Clearly f should use the cursor in some way, otherwise the image will have the same value at every pixel location. Note that map and coflatMap operations will be automatically parallelised. The intuitive idea behind coflatMap is that it extends local computations. For the stream example, the local computation was a linear combination of nearby values. Similarly, in image analysis problems, we often want to apply a linear filter to nearby pixels. We can get at the pixel at the cursor location using extract, but we probably also want to be able to move the cursor around to nearby locations. We can do that by adding some appropriate methods to complete the class definition.

    def up: PImage[T] = {
      val py = y-1
      val ny = if (py >= 0) py else (py + image.h)
      PImage(x,ny,image)
    }
    def down: PImage[T] = {
      val py = y+1
      val ny = if (py < image.h) py else (py - image.h)
      PImage(x,ny,image)
    }
    def left: PImage[T] = {
      val px = x-1
      val nx = if (px >= 0) px else (px + image.w)
      PImage(nx,y,image)
    }
    def right: PImage[T] = {
      val px = x+1
      val nx = if (px < image.w) px else (px - image.w)
      PImage(nx,y,image)
    }
  }

Here each method returns a new pointed image with the cursor shifted by one pixel in the appropriate direction. Note that I’ve used periodic boundary conditions here, which often makes sense for numerical integration of PDEs, but makes less sense for real image analysis problems. Note that we have embedded all “indexing” issues inside the definition of our classes. Now that we have it, none of the statistical algorithms that we develop will involve any explicit indexing. This makes it much less likely to develop algorithms containing bugs corresponding to “off-by-one” or flipped axis errors.

This class is now fine for our requirements. But if we wanted Cats to understand that this structure is really a comonad (perhaps because we wanted to use derived methods, such as coflatten), we would need to provide evidence for this. The details aren’t especially important for this post, but we can do it simply as follows:

  implicit val pimageComonad = new Comonad[PImage] {
    def extract[A](wa: PImage[A]) = wa.extract
    def coflatMap[A,B](wa: PImage[A])(f: PImage[A] => B): PImage[B] =
      wa.coflatMap(f)
    def map[A,B](wa: PImage[A])(f: A => B): PImage[B] = wa.map(f)
  }

It’s handy to have some functions for converting Breeze dense matrices back and forth with our image class.

  import breeze.linalg.{Vector => BVec, _}
  def BDM2I[T](m: DenseMatrix[T]): Image[T] =
    Image(m.cols, m.rows, m.data.toVector.par)
  def I2BDM(im: Image[Double]): DenseMatrix[Double] =
    new DenseMatrix(im.h,im.w,im.data.toArray)

Now we are ready to see how to use this in practice. Let’s start by defining a very simple linear filter.

def fil(pi: PImage[Double]): Double = (2*pi.extract+
  pi.up.extract+pi.down.extract+pi.left.extract+pi.right.extract)/6.0

This simple filter can be used to “smooth” or “blur” an image. However, from a more sophisticated viewpoint, exactly this type of filter can be used to represent one time step of a numerical method for time integration of the 2D heat equation. Now we can simulate a noisy image and apply our filter to it using coflatMap:

import breeze.stats.distributions.Gaussian
val bdm = DenseMatrix.tabulate(200,250){case (i,j) => math.cos(
  0.1*math.sqrt((i*i+j*j))) + Gaussian(0.0,2.0).draw}
val pim0 = PImage(0,0,BDM2I(bdm))
def pims = Stream.iterate(pim0)(_.coflatMap(fil))

Note that here, rather than just applying the filter once, I’ve generated an infinite stream of pointed images, each one representing an additional application of the linear filter. Thus the sequence represents the time solution of the heat equation with initial condition corresponding to our simulated noisy image.

We can render the first few frames to check that it seems to be working.

import breeze.plot._
val fig = Figure("Diffusing a noisy image")
pims.take(25).zipWithIndex.foreach{case (pim,i) => {
  val p = fig.subplot(5,5,i)
  p += image(I2BDM(pim.image))
}}

Note that the numerical integration is carried out in parallel on all available cores automatically. Other image filters can be applied, and other (parabolic) PDEs can be numerically integrated in an essentially similar way.

Gibbs sampling the Ising model

Another place where the concept of extending a local computation to a global computation crops up is in the context of Gibbs sampling a high-dimensional probability distribution by cycling through the sampling of each variable in turn from its full-conditional distribution. I’ll illustrate this here using the Ising model, so that I can reuse the pointed image class from above, but the principles apply to any Gibbs sampling problem. In particular, the Ising model that we consider has a conditional independence structure corresponding to a graph of a square lattice. As above, we will use the comonadic structure of the square lattice to construct a Gibbs sampler. However, we can construct a Gibbs sampler for arbitrary graphical models in an essentially identical way by using a graph comonad.

Let’s begin by simulating a random image containing +/-1s:

import breeze.stats.distributions.{Binomial,Bernoulli}
val beta = 0.4
val bdm = DenseMatrix.tabulate(500,600){
  case (i,j) => (new Binomial(1,0.2)).draw
}.map(_*2 - 1) // random matrix of +/-1s
val pim0 = PImage(0,0,BDM2I(bdm))

We can use this to initialise our Gibbs sampler. We now need a Gibbs kernel representing the update of each pixel.

def gibbsKernel(pi: PImage[Int]): Int = {
   val sum = pi.up.extract+pi.down.extract+pi.left.extract+pi.right.extract
   val p1 = math.exp(beta*sum)
   val p2 = math.exp(-beta*sum)
   val probplus = p1/(p1+p2)
   if (new Bernoulli(probplus).draw) 1 else -1
}

So far so good, but there a couple of issues that we need to consider before we plough ahead and start coflatMapping. The first is that pure functional programmers will object to the fact that this function is not pure. It is a stochastic function which has the side-effect of mutating the random number state. I’m just going to duck that issue here, as I’ve previously discussed how to fix it using probability monads, and I don’t want it to distract us here.

However, there is a more fundamental problem here relating to parallel versus sequential application of Gibbs kernels. coflatMap is conceptually parallel (irrespective of how it is implemented) in that all computations used to build the new comonad are based solely on the information available in the starting comonad. OTOH, detailed balance of the Markov chain will only be preserved if the kernels for each pixel are applied sequentially. So if we coflatMap this kernel over the image we will break detailed balance. I should emphasise that this has nothing to do with the fact that I’ve implemented the pointed image using a parallel vector. Exactly the same issue would arise if we switched to backing the image with a regular (sequential) immutable Vector.

The trick here is to recognise that if we coloured alternate pixels black and white using a chequerboard pattern, then all of the black pixels are conditionally independent given the white pixels and vice-versa. Conditionally independent pixels can be updated by parallel application of a Gibbs kernel. So we just need separate kernels for updating odd and even pixels.

def oddKernel(pi: PImage[Int]): Int =
  if ((pi.x+pi.y) % 2 != 0) pi.extract else gibbsKernel(pi)
def evenKernel(pi: PImage[Int]): Int =
  if ((pi.x+pi.y) % 2 == 0) pi.extract else gibbsKernel(pi)

Each of these kernels can be coflatMapped over the image preserving detailed balance of the chain. So we can now construct an infinite stream of MCMC iterations as follows.

def pims = Stream.iterate(pim0)(_.coflatMap(oddKernel).
  coflatMap(evenKernel))

We can animate the first few iterations with:

import breeze.plot._
val fig = Figure("Ising model Gibbs sampler")
fig.width = 1000
fig.height = 800
pims.take(50).zipWithIndex.foreach{case (pim,i) => {
  print(s"$i ")
  fig.clear
  val p = fig.subplot(1,1,0)
  p.title = s"Ising model: frame $i"
  p += image(I2BDM(pim.image.map{_.toDouble}))
  fig.refresh
}}
println

Here I have a movie showing the first 1000 iterations. Note that youtube seems to have over-compressed it, but you should get the basic idea.

Again, note that this MCMC sampler runs in parallel on all available cores, automatically. This issue of odd/even pixel updating emphasises another issue that crops up a lot in functional programming: very often, thinking about how to express an algorithm functionally leads to an algorithm which parallelises naturally. For general graphs, figuring out which groups of nodes can be updated in parallel is essentially the graph colouring problem. I’ve discussed this previously in relation to parallel MCMC in:

Wilkinson, D. J. (2005) Parallel Bayesian Computation, Chapter 16 in E. J. Kontoghiorghes (ed.) Handbook of Parallel Computing and Statistics, Marcel Dekker/CRC Press, 481-512.

Further reading

There are quite a few blog posts discussing comonads in the context of Haskell. In particular, the post on comonads for image analysis I mentioned previously, and this one on cellular automata. Bartosz’s post on comonads gives some connection back to the mathematical origins. Runar’s Scala comonad tutorial is the best source I know for comonads in Scala.

Full runnable code corresponding to this blog post is available from my blog repo.

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Statistical computing with Scala free on-line course

I’ve recently delivered a three-day intensive short-course on Scala for statistical computing and data science. The course seemed to go well, and the experience has convinced me that Scala should be used a lot more by statisticians and data scientists for a range of problems in statistical computing. In particular, the simplicity of writing fast efficient parallel algorithms is reason alone to take a careful look at Scala. With a view to helping more statisticians get to grips with Scala, I’ve decided to freely release all of the essential materials associated with the course: the course notes (as PDF), code fragments, complete examples, end-of-chapter exercises, etc. Although I developed the materials with the training course in mind, the course notes are reasonably self-contained, making the course quite suitable for self-study. At some point I will probably flesh out the notes into a proper book, but that will probably take me a little while.

I’ve written a brief self-study guide to point people in the right direction. For people studying the material in their spare time, the course is probably best done over nine weeks (one chapter per week), and this will then cover material at a similar rate to a typical MOOC.

The nine chapters are:

1. Introduction
2. Scala and FP Basics
3. Collections
4. Scala Breeze
5. Monte Carlo
6. Statistical modelling
7. Tools
8. Apache Spark
9. Advanced topics

For anyone frustrated by the limitations of dynamic languages such as R, Python or Octave, this course should provide a good pathway to an altogether more sophisticated, modern programming paradigm.

Books on Scala for statistical computing and data science

Introduction

People regularly ask me about books and other resources for getting started with Scala for statistical computing and data science. This post will focus on books, but it’s worth briefly noting that there are a number of other resources available, on-line and otherwise, that are also worth considering. I particularly like the Coursera course Functional Programming Principles in Scala – I still think this is probably the best way to get started with Scala and functional programming for most people. In fact, there is an entire Functional Programming in Scala Specialization that is worth considering – I’ll probably discuss that more in another post. I’ve got a draft page of Scala links which has a bias towards scientific and statistical computing, and I’m currently putting together a short course in that area, which I’ll also discuss further in future posts. But this post will concentrate on books.

Reading list

Getting started with Scala

Before one can dive into statistical computing and data science using Scala, it’s a good idea to understand a bit about the language and about functional programming. There are by now many books on Scala, and I haven’t carefully reviewed all of them, but I’ve looked at enough to have an idea about good ways of getting started.

  • Programming in Scala: Third edition, Odersky et al, Artima.
    • This is the Scala book, often referred to on-line as PinS. It is a weighty tome, and works through the Scala language in detail, starting from the basics. Every serious Scala programmer should own this book. However, it isn’t the easiest introduction to the language.
  • Scala for the Impatient, Horstmann, Addison-Wesley.
    • As the name suggests, this is a much quicker and easier introduction to Scala than PinS, but assumes reasonable familiarity with programming in general, and sort-of assumes that the reader has a basic knowledge of Java and the JVM ecosystem. That said, it does not assume that the reader is a Java expert. My feeling is that for someone who has a reasonable programming background and a passing familiarity with Java, then this book is probably the best introduction to the language. Note that there is a second edition in the works.
  • Functional Programming in Scala Chiusano and Bjarnason, Manning.
    • It is possible to write Scala code in the style of "Java-without-the-semi-colons", but really the whole point of Scala is to move beyond that kind of Object-Oriented programming style. How much you venture down the path towards pure Functional Programming is very much a matter of taste, but many of the best Scala programmers are pretty hard-core FP, and there’s probably a reason for that. But many people coming to Scala don’t have a strong FP background, and getting up to speed with strongly-typed FP isn’t easy for people who only know an imperative (Object-Oriented) style of programming. This is the book that will help you to make the jump to FP. Sometimes referred to online as FPiS, or more often even just as the red book, this is also a book that every serious Scala programmer should own (and read!). Note that is isn’t really a book about Scala – it is a book about strongly typed FP that just "happens" to use Scala for illustrating the ideas. Consequently, you will probably want to augment this book with a book that really is about Scala, such as one of the books above. Since this is the first book on the list published by Manning, I should also mention how much I like computing books from this publisher. They are typically well-produced, and their paper books (pBooks) come with complimentary access to well-produced DRM-free eBook versions, however you purchase them.
  • Functional and Reactive Domain Modeling, Ghosh, Manning.
    • This is another book that isn’t really about Scala, but about software engineering using a strongly typed FP language. But again, it uses Scala to illustrate the ideas, and is an excellent read. You can think of it as a more practical "hands-on" follow-up to the red book, which shows how the ideas from the red book translate into effective solutions to real-world problems.
  • Structure and Interpretation of Computer Programs, second edition Abelson et al, MIT Press.
    • This is not a Scala book! This is the only book in this list which doesn’t use Scala at all. I’ve included it on the list because it is one of the best books on programming that I’ve read, and is the book that I wish someone had told me about 20 years ago! In fact the book uses Scheme (a Lisp derivative) as the language to illustrate the ideas. There are obviously important differences between Scala and Scheme – eg. Scala is strongly statically typed and compiled, whereas Scheme is dynamically typed and interpreted. However, there are also similarities – eg. both languages support and encourage a functional style of programming but are not pure FP languages. Referred to on-line as SICP this book is a classic. Note that there is no need to buy a paper copy if you like eBooks, since electronic versions are available free on-line.

Scala for statistical computing and data science

  • Scala for Data Science, Bugnion, Packt.
    • Not to be confused with the (terrible) book, Scala for machine learning by the same publisher. Scala for Data Science is my top recommendation for getting started with statistical computing and data science applications using Scala. I have reviewed this book in another post, so I won’t say more about it here (but I like it).
  • Scala Data Analysis Cookbook, Manivannan, Packt.
    • I’m not a huge fan of the cookbook format, but this book is really mis-named, as it isn’t really a cookbook and isn’t really about data analysis in Scala! It is really a book about Apache Spark, and proceeds fairly sequentially in the form of a tutorial introduction to Spark. Spark is an impressive piece of technology, and it is obviously one of the factors driving interest in Scala, but it’s important to understand that Spark isn’t Scala, and that many typical data science applications will be better tackled using Scala without Spark. I’ve not read this book cover-to-cover as it offers little over Scala for Data Science, but its coverage of Spark is a bit more up-to-date than the Spark books I mention below, so it could be of interest to those who are mainly interested in Scala for Spark.
  • Scala High Performance Programming, Theron and Diamant, Packt.
    • This is an interesting book, fundamentally about developing high performance streaming data processing algorithm pipelines in Scala. It makes no reference to Spark. The running application is an on-line financial trading system. It takes a deep dive into understanding performance in Scala and on the JVM, and looks at how to benchmark and profile performance, diagnose bottlenecks and optimise code. This is likely to be of more interest to those interested in developing efficient algorithms for scientific and statistical computing rather than applied data scientists, but it covers some interesting material not covered by any of the other books in this list.
  • Learning Spark, Karau et al, O’Reilly.
    • This book provides an introduction to Apache Spark, written by some of the people who developed it. Spark is a big data analytics framework built on top of Scala. It is arguably the best available framework for big data analytics on computing clusters in the cloud, and hence there is a lot of interest in it. The book is a perfectly good introduction to Spark, and shows most examples implemented using the Java and Python APIs in addition to the canonical Scala (Spark Shell) implementation. This is useful for people working with multiple languages, but can be mildly irritating to anyone who is only interested in Scala. However, the big problem with this (and every other) book on Spark is that Spark is evolving very quickly, and so by the time any book on Spark is written and published it is inevitably very out of date. It’s not clear that it is worth buying a book specifically about Spark at this stage, or whether it would be better to go for a book like Scala for Data Science, which has a couple of chapters of introduction to Spark, which can then provide a starting point for engaging with Spark’s on-line documentation (which is reasonably good).
  • Advanced Analytics with Spark, Ryza et al, O’Reilly.
    • This book has a bit of a "cookbook" feel to it, which some people like and some don’t. It’s really more like an "edited volume" with different chapters authored by different people. Unlike Learning Spark it focuses exclusively on the Scala API. The book basically covers the development of a bunch of different machine learning pipelines for a variety of applications. My main problem with this book is that it has aged particularly badly, as all of the pipelines are developed with raw RDDs, which isn’t how ML pipelines in Spark are constructed any more. So again, it’s difficult for me to recommend. The message here is that if you are thinking of buying a book about Spark, check very carefully when it was published and what version of Spark it covers and whether that is sufficiently recent to be of relevance to you.

Summary

There are lots of books to get started with Scala for statistical computing and data science applications. My "bare minimum" recommendation would be some generic Scala book (doesn’t really matter which one), the red book, and Scala for data science. After reading those, you will be very well placed to top-up your knowledge as required with on-line resources.

Scala for Data Science [book review]

This post will review the book:

Disclaimer: This book review has not been solicited by the publisher (or anyone else) in any way. I purchased the review copy of this book myself. I have not received any benefit from the writing of this review.

Introduction

On this blog I previously reviewed the (terrible) book, Scala for machine learning by the same publisher. I was therefore rather wary of buying this book. But the topic coverage looked good, so I decided to buy it, and wasn’t disappointed. Scala for Data Science is my top recommendation for getting started with statistical computing and data science applications using Scala.

Overview

The book assumes a basic familiarity with programming in Scala, at around the level of someone who has completed the Functional Programming Principles in Scala Coursera course. That is, it (quite sensibly) doesn’t attempt to teach the reader how to program in Scala, but rather how to approach the development of data science applications using Scala. It introduces more advanced Scala idioms gradually (eg. typeclasses don’t appear until Chapter 5), so it is relatively approachable for those who aren’t yet Scala experts. The book does cover Apache Spark, but Spark isn’t introduced until Chapter 10, so it isn’t “just another Spark book”. Most of the book is about developing data science applications in Scala, completely independently of Spark. That said, it also provides one of the better introductions to Spark, so doubles up as a pretty good introductory Spark book, in addition to being a good introduction to the development of data science applications with Scala. It should probably be emphasised that the book is very much focused on data science, rather than statistical computing, but there is plenty of material of relevance to those who are more interested in statistical computing than applied data science.

Chapter by chapter

  1. Scala and Data Science – motivation for using Scala in preference to certain other languages I could mention…
  2. Manipulating data with BreezeBreeze is the standard Scala library for scientific and statistical computing. It’s pretty good, but documentation is rather lacking. This Chapter provides a good tutorial introduction to Breeze, which should be enough to get people going sufficiently to be able to make some sense of the available on-line documentation.
  3. Plotting with breeze-viz – Breeze has some support for plotting and visualisation of data. It’s somewhat limited when compared to what is available in R, but is fine for interactive exploratory analysis. However, the available on-line documentation for breeze-viz is almost non-existent. This Chapter is the best introduction to breeze-viz that I have seen.
  4. Parallel collections and futures – the Scala standard library has built-in support for parallel and concurrent programming based on functional programming concepts such as parallel (monadic) collections and Futures. Again, this Chapter provides an excellent introduction to these powerful concepts, allowing the reader to start developing parallel algorithms for multi-core hardware with minimal fuss.
  5. Scala and SQL through JDBC – this Chapter looks at connecting to databases using standard JVM mechanisms such as JDBC. However, it gradually introduces more functional ways of interfacing with databases using typeclasses, motivating:
  6. Slick – a functional interface for SQL – an introduction to the Slick library for a more Scala-esque way of database interfacing.
  7. Web APIs – the practicalities of talking to web APIs. eg. authenticated HTTP requests and parsing of JSON responses.
  8. Scala and MongoDB – working with a NoSQL database from Scala
  9. Concurrency with Akka – Akka is the canonical implementation of the actor model in Scala, for building large concurrent applications. It is the foundation on which Spark is built.
  10. Distributed batch processing with Spark – a tutorial introduction to Apache Spark. Spark is a big data analytics framework built on top of Scala and Akka. It is arguably the best available framework for big data analytics on computing clusters in the cloud, and hence there is a lot of interest in it. Indeed, Spark is driving some of the interest in Scala.
  11. Spark SQL and DataFrames – interfacing with databases using Spark, and more importantly, an introduction to Spark’s DataFrame abstraction, which is now fundamental to developing machine learning pipelines in Spark.
  12. Distributed machine learning with MLLib – MLLib is the machine learning library for Spark. It is worth emphasising that unlike many early books on Spark, this chapter covers the newer DataFrame-based pipeline API, in addition to the original RDD-based API. Together, Chapters 10, 11 and 12 provide a pretty good tutorial introduction to Spark. After working through these, it should be easy to engage with the official on-line Spark documentation.
  13. Web APIs with Play – is concerned with developing a web API at the end of a data science pipeline.
  14. Visualisation with D3 and the Play framework – is concerned with integrating visualisation into a data science web application.

Summary

This book provides a good tutorial introduction to a large number of topics relevant to statisticians and data scientists interested in developing data science applications using Scala. After working through this book, readers should be well-placed to augment their knowledge with readily searchable on-line documentation.

In a follow-up post I will give a quick overview of some other books relevant to getting started with Scala for statistical computing and data science.

First steps with monads in Scala

Introduction

In the previous post I gave a quick introduction to some important concepts in functional programming, such as HOFs, closures, currying and partial application, and hopefully gave some insight into why these concepts might be useful in the context of scientific computing. Another concept that is very important in modern functional programming is that of the monad. Monads are one of those concepts that turns out to be very simple and intuitive once you “get it”, but completely impenetrable until you do! Now, there zillions of monad tutorials out there, and I don’t think that I have anything particularly insightful to add to the discussion. That said, most of the tutorials focus on problems and examples that are some way removed from the interests of statisticians and scientific programmers. So in this post I want to try and give a very informal and intuitive introduction to the monad concept in a way that I hope will resonate with people from a more scientific computing background.

The term “monad” is borrowed from that of the corresponding concept in category theory. The connection between functional programming and category theory is strong and deep. I intend to expore this more in future posts, but for this post the connection is not important and no knowledge of category theory is assumed (or imparted!).

Functors and Monads

Maps and Functors

All of the code used in this post in contained in the first-monads directory of my blog repo. The best way to follow this post is to copy-and-paste commands one-at-a-time from this post to a Scala REPL or sbt console. Note that only the numerical linear algebra examples later in this post require any non-standard dependencies.

The map method is one of the first concepts one meets when beginning functional programming. It is a higher order method on many (immutable) collection and other container types. Let’s start by looking at how map operates on Lists.

val x = (0 to 4).toList
// x: List[Int] = List(0, 1, 2, 3, 4)
val x2 = x map { x => x * 3 }
// x2: List[Int] = List(0, 3, 6, 9, 12)
val x3 = x map { _ * 3 }
// x3: List[Int] = List(0, 3, 6, 9, 12)
val x4 = x map { _ * 0.1 }
// x4: List[Double] = List(0.0, 0.1, 0.2, 0.30000000000000004, 0.4)

The last example shows that a List[T] can be converted to a List[S] if map is passed a function of type T => S. Of course there’s nothing particularly special about List here. It works with other collection types in the same way, as the following example with (immutable) Vector illustrates:

val xv = x.toVector
// xv: Vector[Int] = Vector(0, 1, 2, 3, 4)
val xv2 = xv map { _ * 0.2 }
// xv2: scala.collection.immutable.Vector[Double] = Vector(0.0, 0.2, 0.4, 0.6000000000000001, 0.8)
val xv3 = for (xi <- xv) yield (xi * 0.2)
// xv3: scala.collection.immutable.Vector[Double] = Vector(0.0, 0.2, 0.4, 0.6000000000000001, 0.8)

Note here that the for comprehension generating xv3 is exactly equivalent to the map call generating xv2 – the for-comprehension is just syntactic sugar for the map call. The benefit of this syntax will become apparent in the more complex examples we consider later.

Many collection and other container types have a map method that behaves this way. Any parametrised type that does have a map method like this is known as a Functor. Again, the name is due to category theory, but that doesn’t matter for this post. From a Scala-programmer perspective, a functor can be thought of as a trait, in pseudo-code as

trait F[T] {
  def map(f: T => S): F[S]
}

with F representing the functor. In fact it turns out to be better to think of a functor as a type class, but that is yet another topic for a future post… Also note that to be a functor in the strict sense (from a category theory perspective), the map method must behave sensibly – that is, it must satisfy the functor laws. But again, I’m keeping things informal and intuitive for this post – there are plenty of other monad tutorials which emphasise the category theory connections.

FlatMap and Monads

Once we can map functions over elements of containers, we soon start mapping functions which themselves return values of the container type. eg. we can map a function returning a List over the elements of a List, as illustrated below.

val x5 = x map { x => List(x - 0.1, x + 0.1) }
// x5: List[List[Double]] = List(List(-0.1, 0.1), List(0.9, 1.1), List(1.9, 2.1), List(2.9, 3.1), List(3.9, 4.1))

Clearly this returns a list-of-lists. Sometimes this is what we want, but very often we actually want to flatten down to a single list so that, for example, we can subsequently map over all of the elements of the base type with a single map. We could take the list-of-lists and then flatten it, but this pattern is so common that the act of mapping and then flattening is often considered to be a basic operation, often known in Scala as flatMap. So for our toy example, we could carry out the flatMap as follows.

val x6 = x flatMap { x => List(x - 0.1, x + 0.1) }
// x6: List[Double] = List(-0.1, 0.1, 0.9, 1.1, 1.9, 2.1, 2.9, 3.1, 3.9, 4.1)

The ubiquity of this pattern becomes more apparent when we start thinking about iterating over multiple collections. For example, suppose now that we have two lists, x and y, and that we want to iterate over all pairs of elements consisting of one element from each list.

val y = (0 to 12 by 2).toList
// y: List[Int] = List(0, 2, 4, 6, 8, 10, 12)
val xy = x flatMap { xi => y map { yi => xi * yi } }
// xy: List[Int] = List(0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 6, 8, 10, 12, 0, 4, 8, 12, 16, 20, 24, 0, 6, 12, 18, 24, 30, 36, 0, 8, 16, 24, 32, 40, 48)

This pattern of having one or more nested flatMaps followed by a final map in order to iterate over multiple collections is very common. It is exactly this pattern that the for-comprehension is syntactic sugar for. So we can re-write the above using a for-comprehension

val xy2 = for {
  xi <- x
  yi <- y
} yield (xi * yi)
// xy2: List[Int] = List(0, 0, 0, 0, 0, 0, 0, 0, 2, 4, 6, 8, 10, 12, 0, 4, 8, 12, 16, 20, 24, 0, 6, 12, 18, 24, 30, 36, 0, 8, 16, 24, 32, 40, 48)

This for-comprehension (usually called a for-expression in Scala) has an intuitive syntax reminiscent of the kind of thing one might write in an imperative language. But it is important to remember that <- is not actually an imperative assignment. The for-comprehension really does expand to the pure-functional nested flatMap and map call given above.

Recalling that a functor is a parameterised type with a map method, we can now say that a monad is just a functor which also has a flatMap method. We can write this in pseudo-code as

trait M[T] {
  def map(f: T => S): M[S]
  def flatMap(f: T => M[S]): M[S]
}

Not all functors can have a flattening operation, so not all functors are monads, but all monads are functors. Monads are therefore more powerful than functors. Of course, more power is not always good. The principle of least power is one of the main principles of functional programming, but monads are useful for sequencing dependent computations, as illustrated by for-comprehensions. In fact, since for-comprehensions de-sugar to calls to map and flatMap, monads are precisely what are required in order to be usable in for-comprehensions. Collections supporting map and flatMap are referred to as monadic. Most Scala collections are monadic, and operating on them using map and flatMap operations, or using for-comprehensions is referred to as monadic-style. People will often refer to the monadic nature of a collection (or other container) using the word monad, eg. the “List monad”.

So far the functors and monads we have been working with have been collections, but not all monads are collections, and in fact collections are in some ways atypical examples of monads. Many monads are containers or wrappers, so it will be useful to see examples of monads which are not collections.

Option monad

One of the first monads that many people encounter is the Option monad (referred to as the Maybe monad in Haskell, and Optional in Java 8). You can think of it as being a strange kind of “collection” that can contain at most one element. So it will either contain an element or it won’t, and so can be used to wrap the result of a computation which might fail. If the computation succeeds, the value computed can be wrapped in the Option (using the type Some), and if it fails, it will not contain a value of the required type, but simply be the value None. It provides a referentially transparent and type-safe alternative to raising exceptions or returning NULL references. We can transform Options using map.

val three = Option(3)
// three: Option[Int] = Some(3)
val twelve = three map (_ * 4)
// twelve: Option[Int] = Some(12)

But when we start combining the results of multiple computations that could fail, we run into exactly the same issues as before.

val four = Option(4)
// four: Option[Int] = Some(4)
val twelveB = three map (i => four map (i * _))
// twelveB: Option[Option[Int]] = Some(Some(12))

Here we have ended up with an Option wrapped in another Option, which is not what we want. But we now know the solution, which is to replace the first map with flatMap, or better still, use a for-comprehension.

val twelveC = three flatMap (i => four map (i * _))
// twelveC: Option[Int] = Some(12)
val twelveD = for {
  i <- three
  j <- four
} yield (i * j)
// twelveD: Option[Int] = Some(12)

Again, the for-comprehension is a little bit easier to understand than the chaining of calls to flatMap and map. Note that in the for-comprehension we don’t worry about whether or not the Options actually contain values – we just concentrate on the “happy path”, where they both do, safe in the knowledge that the Option monad will take care of the failure cases for us. Two of the possible failure cases are illustrated below.

val oops: Option[Int] = None
// oops: Option[Int] = None
val oopsB = for {
  i <- three
  j <- oops
} yield (i * j)
// oopsB: Option[Int] = None
val oopsC = for {
  i <- oops
  j <- four
} yield (i * j)
// oopsC: Option[Int] = None

This is a typical benefit of code written in a monadic style. We chain together multiple computations thinking only about the canonical case and trusting the monad to take care of any additional computational context for us.

IEEE floating point and NaN

Those with a background in scientific computing are probably already familiar with the NaN value in IEEE floating point. In many regards, this value and the rules around its behaviour mean that Float and Double types in IEEE compliant languages behave as an Option monad with NaN as the None value. This is simply illustrated below.

val nan = Double.NaN
3.0 * 4.0
// res0: Double = 12.0
3.0 * nan
// res1: Double = NaN
nan * 4.0
// res2: Double = NaN

The NaN value arises naturally when computations fail. eg.

val nanB = 0.0 / 0.0
// nanB: Double = NaN

This Option-like behaviour of Float and Double means that it is quite rare to see examples of Option[Float] or Option[Double] in Scala code. But there are some disadvantages of the IEEE approach, as discussed elsewhere. Also note that this only works for Floats and Doubles, and not for other types, including, say, Int.

val nanC=0/0
// This raises a runtime exception!

Option for matrix computations

Good practical examples of scientific computations which can fail crop up frequently in numerical linear algebra, so it’s useful to see how Option can simplify code in that context. Note that the code in this section requires the Breeze library, so should be run from an sbt console using the sbt build file associated with this post.

In statistical applications, one often needs to compute the Cholesky factorisation of a square symmetric matrix. This operation is built into Breeze as the function cholesky. However the factorisation will fail if the matrix provided is not positive semi-definite, and in this case the cholesky function will throw a runtime exception. We will use the built in cholesky function to create our own function, safeChol (using a monad called Try which is closely related to the Option monad) returning an Option of a matrix rather than a matrix. This function will not throw an exception, but instead return None in the case of failure, as illustrated below.

import breeze.linalg._
def safeChol(m: DenseMatrix[Double]): Option[DenseMatrix[Double]] = scala.util.Try(cholesky(m)).toOption
val m = DenseMatrix((2.0, 1.0), (1.0, 3.0))
val c = safeChol(m)
// c: Option[breeze.linalg.DenseMatrix[Double]] =
// Some(1.4142135623730951  0.0
// 0.7071067811865475  1.5811388300841898  )

val m2 = DenseMatrix((1.0, 2.0), (2.0, 3.0))
val c2 = safeChol(m2)
// c2: Option[breeze.linalg.DenseMatrix[Double]] = None

A Cholesky factorisation is often followed by a forward or backward solve. This operation may also fail, independently of whether the Cholesky factorisation fails. There doesn’t seem to be a forward solve function directly exposed in the Breeze API, but we can easily define one, which I call dangerousForwardSolve, as it will throw an exception if it fails, just like the cholesky function. But just as for the cholesky function, we can wrap up the dangerous function into a safe one that returns an Option.

import com.github.fommil.netlib.BLAS.{getInstance => blas}
def dangerousForwardSolve(A: DenseMatrix[Double], y: DenseVector[Double]): DenseVector[Double] = {
  val yc = y.copy
  blas.dtrsv("L", "N", "N", A.cols, A.toArray, A.rows, yc.data, 1)
  yc
}
def safeForwardSolve(A: DenseMatrix[Double], y: DenseVector[Double]): Option[DenseVector[Double]] = scala.util.Try(dangerousForwardSolve(A, y)).toOption

Now we can write a very simple function which chains these two operations together, as follows.

def safeStd(A: DenseMatrix[Double], y: DenseVector[Double]): Option[DenseVector[Double]] = for {
  L <- safeChol(A)
  z <- safeForwardSolve(L, y)
} yield z

safeStd(m,DenseVector(1.0,2.0))
// res15: Option[breeze.linalg.DenseVector[Double]] = Some(DenseVector(0.7071067811865475, 0.9486832980505138))

Note how clean and simple this function is, concentrating purely on the “happy path” where both operations succeed and letting the Option monad worry about the three different cases where at least one of the operations fails.

The Future monad

Let’s finish with a monad for parallel and asynchronous computation, the Future monad. The Future monad is used for wrapping up slow computations and dispatching them to another thread for completion. The call to Future returns immediately, allowing the calling thread to continue while the additional thread processes the slow work. So at that stage, the Future will not have completed, and will not contain a value, but at some (unpredictable) time in the future it (hopefully) will (hence the name). In the following code snippet I construct two Futures that will each take at least 10 seconds to complete. On the main thread I then use a for-comprehension to chain the two computations together. Again, this will return immediately returning another Future that at some point in the future will contain the result of the derived computation. Then, purely for illustration, I force the main thread to stop and wait for that third future (f3) to complete, printing the result to the console.

import scala.concurrent.duration._
import scala.concurrent.{Future,ExecutionContext,Await}
import ExecutionContext.Implicits.global
val f1=Future{
  Thread.sleep(10000)
  1 }
val f2=Future{
  Thread.sleep(10000)
  2 }
val f3=for {
  v1 <- f1
  v2 <- f2
  } yield (v1+v2)
println(Await.result(f3,30.second))

When you paste this into your console you should observe that you get the result in 10 seconds, as f1 and f2 execute in parallel on separate threads. So the Future monad is one (of many) ways to get started with parallel and async programming in Scala.

Summary

In this post I’ve tried to give a quick informal introduction to the monad concept, and tried to use examples that will make sense to those interested in scientific and statistical computing. There’s loads more to say about monads, and there are many more commonly encountered useful monads that haven’t been covered in this post. I’ve skipped over lots of details, especially those relating to the formal definitions of functors and monads, including the laws that map and flatMap must satisfy and why. But those kinds of details can be easily picked up from other monad tutorials. Anyone interested in pursuing the formal connections may be interested in a page of links I’m collating on category theory for FP. In particular, I quite like the series of blog posts on category theory for programmers. As I’ve mentioned in previous posts, I also really like the book Functional Programming in Scala, which I strongly recommend to anyone who wants to improve their Scala code. In a subsequent post I’ll explain how monadic style is relevant to issues relating to the statistical analysis of big data, as exemplified in Apache Spark. It’s probably also worth mentioning that there is another kind of functor that turns out to be exceptionally useful in functional programming: the applicative functor. This is more powerful than a basic functor, but less powerful than a monad. It turns out to be useful for computations which need to be sequenced but are not sequentially dependent (context-free rather than context-sensitive), and is a little bit more general and flexible than a monad in cases where it is appropriate.

HOFs, closures, partial application and currying to solve the function environment problem in Scala

Introduction

Functional programming (FP) is a programming style that emphasises the use of referentially transparent pure functions and immutable data structures. Higher order functions (HOFs) tend to be used extensively to enable a clean functional programming style. A HOF is just a function that either takes a function as an argument or returns a function. For example, the default List type in Scala is immutable. So, if one defines a list via

val l1 = List(1,2,3)

we add a new value to the front of the list by creating a new list from the old list and leaving the old list unchanged:

val l2 = 4 :: l1
// List(4, 1, 2, 3)

We can create a new list the same length as an existing list by applying the same function to each element of the list in turn using map:

val l3 = l2 map { x => x*x }
// List(16, 1, 4, 9)

We could write this slightly differently as

val l4 = l2.map(x => x*x)

which makes it clearer that map is a higher order function on lists. In fact, the presence of a map method on List[_] makes it a functor, but that is a topic for another post.

HOFs are ubiquitous in FP, and very powerful. But there are a few techniques for working with functions in Scala (and other FP languages) which make creating and using HOFs more convenient.

Plotting a function of one scalar variable

There are many, many reasons for using functions and HOFs in scientific and statistical computing (optimising, integrating, differentiating, or sampling, to name just a few). But the basic idea can be illustrated simply by considering the problem of plotting a function of one scalar variable.

All of the code associated with this post is available in the curry directory of my blog repo. Full instructions for running the code are included in the README. The code includes a simple short method, plotFun which uses breeze to produce a simple plot of a user supplied function. For example:

import Currying._

plotFun(x => x*x)

produces the plot:

Quadratic Plot

We can use this method to plot other functions, for example:

def myQuad1(x: Double): Double = x*x - 2*x + 1
plotFun(myQuad1)
def myQuad2(x: Double): Double = x*x - 3*x - 1
plotFun(myQuad2)

Now technically, myQuad1 and myQuad2 are methods rather than functions. The distinction is a bit subtle, and they can often be used interchangeably, but the difference does sometimes matter, so it is good to understand it. To actually define a function and plot it, we could instead use code like:

val myQuad3: (Double => Double) = x => -x*x + 2
plotFun(myQuad3)

Note that here myQuad3 is a value whose type is a function mapping a Double to a Double. This is a proper function. This style of function declaration should make sense to people coming from other functional languages such as Haskell, but is potentially confusing to those coming from O-O languages such as Java. Note that is is easy to convert a method to a function using an underscore, so that, for example, myQuad2 _ will give the function corresponding to myQuad2. Note that there is no corresponding way to get a method from a function, so that is one reason for preferring method declaration syntax (and there are others, such as the ability to parametrise method declarations with generic types).

Now, rather than defining lots of specific instances of quadratic functions from scratch, it would make more sense to define a generic quadratic function and then just plot particular instances of this generic quadratic. It is simple enough to define a generic quadratic with:

def quadratic(a: Double, b: Double, c: Double, x: Double): Double = 
  a*x*x + b*x + c

But we clearly can’t pass that in to the plotting function directly, as it has the wrong type signature (not Double => Double), and the specific values of a, b and c need to be given. This issue crops up a lot in scientific and statistical computing – there is a function which has some additional parameters which need to be fixed before the function can actually be used. This is referred to as the “function environment problem” by Oliveira and Stewart (section 8.5). Fortunately, in functional languages it’s easy enough to use this function to create a new “partially specified” function and pass that in. For example, we could just do

plotFun(x => quadratic(3,2,1,x))

We can define another function, quadFun, which allows us to construct these partially applied function closures, and use it as follows:

def quadFun(a: Double, b: Double, c: Double): Double => Double = 
  x => quadratic(a,b,c,x)
val myQuad4 = quadFun(2,1,3)
plotFun(myQuad4)
plotFun(quadFun(1,2,3))

Here, quadFun is a HOF in the sense that it returns a function closure corresponding to the partially applied quadratic function. The returned function has the type Double => Double, so we can use it wherever a function with this signature is expected. Note that the function carries around with it its lexical “environment”, specifically, the values of a, b and c specified at the time quadFun was called. This style of constructing closures works in most lexically scoped languages which have functions as first class objects. I use this style of programming a lot in several different languages. In particular, I’ve written previously about lexical scope and function closures in R.

Again, the intention is perhaps slightly more explicit if we re-write the above using function syntax as:

val quadFunF: (Double,Double,Double) => Double => Double = 
  (a,b,c) => x => quadratic(a,b,c,x)
val myQuad5 = quadFunF(-1,1,2)
plotFun(myQuad5)
plotFun(quadFunF(1,-2,3))

Now, this concept of partial application is so prevalent in FP that some languages have special syntactic support for it. In Scala, we can partially apply using an underscore to represent unapplied parameters as:

val myQuad6 = quadratic(1,2,3,_: Double)
plotFun(myQuad6)

In Scala we can also directly write our functions in curried form, with parameters (or parameter lists) ordered as they are to be applied. So, for this example, we could define (partially) curried quad and use it with:

def quad(a: Double, b: Double, c: Double)(x: Double): Double = a*x*x + b*x + c
plotFun(quad(1,2,-3))
val myQuad7 = quad(1,0,1) _
plotFun(myQuad7)

Note the use of an underscore to convert a partially applied method to a function. Also note that every function has a method curried which turns an uncurried function into a (fully) curried function. So in the case of our quadratic function, the fully curried version will be a chain of four functions.

def quadCurried = (quadratic _).curried
plotFun(quadCurried(1)(2)(3))

Again, note the strategic use of an underscore. The underscore isn’t necessary if we have a true function to start with, as the following illustrates:

val quadraticF: (Double,Double,Double,Double) => Double = (a,b,c,x) => a*x*x + b*x + c
def quadCurried2 = quadraticF.curried
plotFun(quadCurried2(-1)(2)(3))

Summary

Working with functions, closures, HOFs and partial application is fundamental to effective functional programming style. Currying functions is one approach to handling the function environment problem, and is the standard approach in languages such as Haskell. However, in Scala there are other possible approaches, such as using underscores for partial application. The preferred approach will depend on the context. Currying is often used for HOFs accepting a function as argument (as it can help with type inference), and also in conjunction with implicits (beyond the scope of this post – pun intended). In other contexts partial application using underscores seems to be more commonly used.

References

A functional Gibbs sampler in Scala

For many years I’ve had a passing interest in functional programming and languages which support functional programming approaches. I’m also quite interested in MOOCs and their future role in higher education. So I recently signed up for my first on-line course, Functional Programming Principles in Scala, via Coursera. I’m around half way through the course at the time of writing, and I’m enjoying it very much. I knew that I didn’t know much about Scala before starting the course, but during the course I’ve also learned that I didn’t know as much about functional programming as I thought I did, either! 😉 The course itself is very interesting, the assignments are well designed and appropriately challenging, and the web infrastructure to support the course is working well. I suspect I’ll try other on-line courses in the future.

Functional programming emphasises immutability, and discourages imperative programming approaches that use variables that can be modified during run-time. There are many advantages to immutability, especially in the context of parallel and concurrent programming, which is becoming increasingly important as multi-core systems become the norm. I’ve always found functional programming to be intellectually appealing, but have often worried about the practicalities of using functional programming in the context of scientific computing where many algorithms are iterative in nature, and are typically encoded using imperative approaches. The Scala programming language is appealing to me as it supports both imperative and functional styles of programming, as well as object oriented approaches. However, as a result of taking this course I am now determined to pursue functional approaches further, and get more of a feel for how practical they are for scientific computing applications.

For my first experiment, I’m going back to my post describing a Gibbs sampler in various languages. See that post for further details of the algorithm. In that post I did have an example implementation in Scala, which looked like this:

object GibbsSc {
 
    import cern.jet.random.tdouble.engine.DoubleMersenneTwister
    import cern.jet.random.tdouble.Normal
    import cern.jet.random.tdouble.Gamma
    import Math.sqrt
    import java.util.Date
 
    def main(args: Array[String]) {
        val N=50000
        val thin=1000
        val rngEngine=new DoubleMersenneTwister(new Date)
        val rngN=new Normal(0.0,1.0,rngEngine)
        val rngG=new Gamma(1.0,1.0,rngEngine)
        var x=0.0
        var y=0.0
        println("Iter x y")
        for (i <- 0 until N) {
            for (j <- 0 until thin) {
                x=rngG.nextDouble(3.0,y*y+4)
                y=rngN.nextDouble(1.0/(x+1),1.0/sqrt(2*x+2))
            }
            println(i+" "+x+" "+y)
        }
    }
 
}

At the time I wrote that post I knew even less about Scala than I do now, so I created the code by starting from the Java version and removing all of the annoying clutter! 😉 Clearly this code has an imperative style, utilising variables (declared with var) x and y having mutable state that is updated by a nested for loop. This algorithm is typical of the kind I use every day, so if I can’t re-write this in a more functional style, removing all mutable variables from my code, then I’m not going to get very far with functional programming!

In fact it is very easy to re-write this in a more functional style without utilising mutable variables. One possible approach is presented below.

object FunGibbs {
 
    import cern.jet.random.tdouble.engine.DoubleMersenneTwister
    import cern.jet.random.tdouble.Normal
    import cern.jet.random.tdouble.Gamma
    import java.util.Date
    import scala.math.sqrt

    val rngEngine=new DoubleMersenneTwister(new Date)
    val rngN=new Normal(0.0,1.0,rngEngine)
    val rngG=new Gamma(1.0,1.0,rngEngine)

    class State(val x: Double,val y: Double)

    def nextIter(s: State): State = {
         val newX=rngG.nextDouble(3.0,(s.y)*(s.y)+4.0)
         new State(newX, 
              rngN.nextDouble(1.0/(newX+1),1.0/sqrt(2*newX+2)))
    }

    def nextThinnedIter(s: State,left: Int): State = {
       if (left==0) s 
       else nextThinnedIter(nextIter(s),left-1)
    }

    def genIters(s: State,current: Int,stop: Int,thin: Int): State = {
         if (!(current>stop)) {
             println(current+" "+s.x+" "+s.y)
             genIters(nextThinnedIter(s,thin),current+1,stop,thin)
         }
         else s
    }

    def main(args: Array[String]) {
        println("Iter x y")
        genIters(new State(0.0,0.0),1,50000,1000)
     }

}

Although it is a few lines longer, it is a fairly clean implementation, and doesn’t look like a hack. Like many functional programs, this one makes extensive use of recursion. This is one of the things that has always concerned me about functional programming – many scientific computing applications involve lots of iteration, and that can potentially translate into very deep recursion. The above program has an apparent recursion depth of 50 million! However, it runs fine without crashing despite the fact that most programming languages will crash out with a stack overflow with recursion depths of more than a couple of thousand. So why doesn’t this crash? It runs fine because the recursion I used is a special form of recursion known as a tail call. Most functional (and some imperative) programming languages automatically perform tail call elimination which essentially turns the tail call into an iteration which runs very fast without creating new stack frames. In fact, this functional version of the code runs at roughly the same speed as the iterative version I presented first (perhaps just a few percent slower – I haven’t done careful timings), and runs well within a factor of 2 of imperative C code. So actually this seems perfectly practical so far, and I’m looking forward to experimenting more with functional programming approaches to statistical computation over the coming months…