Mathias Brandewinder on .NET, F#, VSTO and Excel development, and quantitative analysis / machine learning.
15. February 2014 12:51

My favorite column in MSDN Magazine is Test Run; it was originally focused on testing, but the author, James McCaffrey, has been focusing lately on topics revolving around numeric optimization and machine learning, presenting a variety of methods and approaches. I quite enjoy his work, with one minor gripe –his examples are all coded in C#, which in my opinion is really too bad, because the algorithms would gain much clarity if written in F# instead.

Back in June 2013, he published a piece on Amoeba Method Optimization using C#. I hadn’t seen that approach before, and found it intriguing. I also found the C# code a bit too hairy for my feeble brain to follow, so I decided to rewrite it in F#.

In a nutshell, the Amoeba approach is a heuristic to find the minimum of a function. Its proper respectable name is the Nelder-Nead method. The reason it is also called the Amoeba method is because of the way the algorithm works: in its simple form, it starts from a triangle, the “Amoeba”; at each step, the Amoeba “probes” the value of 3 points in its neighborhood, and moves based on how much better the new points are. As a result, the triangle is iteratively updated, and behaves a bit like an Amoeba moving on a surface.

Before going into the actual details of the algorithm, here is how my final result looks like. You can find the entire code here on GitHub, with some usage examples in the Sample.fsx script file. Let’s demo the code in action: in a script file, we load the Amoeba code, and use the same function the article does, the Rosenbrock function. We transform the function a bit, so that it takes a Point (an alias for an Array of floats, essentially a vector) as an input, and pass it to the solve function, with the domain where we want to search, in that case, [ –10.0; 10.0 ] for both x and y:

#load "Amoeba.fs"

open Amoeba
open Amoeba.Solver

let g (x:float) y =
100. * pown (y - x * x) 2 + pown (1. - x) 2

let testFunction (x:Point) =
g x.[0] x.[1]

solve Default [| (-10.,10.); (-10.,10.) |] testFunction 1000

Running this in the F# interactive window should produce the following:

val it : Solution = (0.0, [|1.0; 1.0|])
>

The algorithm properly identified that the minimum is 0, for a value of x = 1.0 and y = 1.0. Note that results may vary: this is a heuristic, which starts with a random initial amoeba, so each run could produce slightly different results, and might at times epically fail.

More...

26. May 2013 09:06

I got interested in the following question lately: given a data set of examples with some continuous-valued features and discrete classes, what’s a good way to reduce the continuous features into a set of discrete values?

What makes this question interesting? One very specific reason is that some machine learning algorithms, like Decision Trees, require discrete features. As a result, potentially informative data has to be discarded. For example, consider the Titanic dataset: we know the age of passengers of the Titanic, or how much they paid for their ticket. To use these features, we would need to reduce them to a set of states, like “Old/Young” or “Cheap/Medium/Expensive” – but how can we determine what states are appropriate, and what values separate them?

More generally, it’s easier to reason about a handful of cases than a continuous variable – and it’s also more convenient computationally to represent information as a finite set states.

So how could we go about identifying a reasonable way to partition a continuous variable into a handful of informative, representative states?

In the context of a classification problem, what we are interested in is whether the states provide information with respect to the Classes we are trying to recognize. As far as I can tell from my cursory review of what’s out there, the main approaches use either Chi-Square tests or Entropy to achieve that goal. I’ll leave aside Chi-Square based approaches for today, and look into the Recursive Minimal Entropy Partitioning algorithm proposed by Fayyad & Irani in 1993.

## The algorithm idea

The algorithm hinges on two key ideas:

• Data should be split into intervals that maximize the information, measured by Entropy,
• Partitioning should not be too fine-grained, to avoid over-fitting.

The first part is classic: given a data set, split in two halves, based on whether the continuous value is above or below the “splitting value”, and compute the gain in entropy. Out of all possibly splitting values, take the one that generates the best gain – and repeat in a recursive fashion.

Let’s illustrate on an artificial example – our output can take 2 values, Yes or No, and we have one continuous-valued feature:

 Continuous Feature Output Class 1.0 Yes 1.0 Yes 2.0 No 3.0 Yes 3.0 No

As is, the dataset has an Entropy of H = - 0.6 x Log (0.6) – 0.4 x Log (0.4) = 0.67 (5 examples, with 3/5 Yes, and 2/5 No).

The Continuous Feature takes 3 values: 1.0, 2.0 and 3.0, which leaves us with 2 possible splits: strictly less than 2, or strictly less than 3. Suppose we split on 2.0 – we would get 2 groups. Group 1 contains Examples where the Feature is less than 2:

 Continuous Feature Output Class 1.0 Yes 1.0 Yes

The Entropy of Group 1 is H(g1) = - 1.0 x Log(1.0) = 0.0

Group 2 contains the rest of the examples:

 Continuous Feature Output Class 2.0 No 3.0 Yes 3.0 No

The Entropy of Group 2 is H(g2) = - 0.33 x Log(0.33) – 0.66 x Log(0.66) = 0.63

Partitioning on 2.0 gives us a gain of H – 2/5 x H(g1) – 3/5 x H(g2) = 0.67 – 0.4 x 0.0 – 0.6 x 0.63 = 0.04. That split gives us additional information on the output, which seems intuitively correct, as one of the groups is now formed purely of “Yes”. In a similar fashion, we can compute the information gain of splitting around the other possible value, 3.0, which would give us a gain of 0.67 – 0.6 x 0.63 – 0.4 x 0.69 =  - 0.00: that split doesn’t improve information, so we would use the first split (or, if we had multiple splits with positive gain, we would take the split leading to the largest gain).

So why not just recursively apply that procedure, and split our dataset until we cannot achieve information gain by splitting further? The issue is that we might end up with an artificially fine-grained partition, over-fitting the data.

More...

28. April 2013 09:32

In our previous post, we began exploring Singular Value Decomposition (SVD) using Math.NET and F#, and showed how this linear algebra technique can be used to “extract” the core information of a dataset and construct a reduced version of the dataset with limited loss of information.

Today, we’ll pursue our excursion in Chapter 14 of Machine Learning in Action, and look at how this can be used to build a collaborative recommendation engine. We’ll follow the approach outlined by the book, starting first with a “naïve” approach, and then using an SVD-based approach.

We’ll start from a slightly modified setup from last post, loosely inspired by the Netflix Prize. The full code for the example can be found here on GitHub.

## The problem and setup

In the early 2000s, Netflix had an interesting problem. Netflix’s business model was simple: you would subscribe, and for a fixed fee you could watch as many movies from their catalog as you wanted. However, what happened was the following: users would watch all the movies they knew they wanted to watch, and after a while, they would run out of ideas – and rather than search for lesser-known movies, they would leave. As a result, Netflix launched a prize: if you could create a model that could provide users with good recommendations for new movies to watch, you could claim a \$1,000,000 prize.

Obviously, we won’t try to replicate the Netflix prize here, if only because the dataset was rather large; 500,000 users and 20,000 movies is a lot of data… We will instead work off a fake, simplified dataset that illustrates some of the key ideas behind collaborative recommendation engines, and how SVD can help in that context. For the sake of clarity, I’ll be erring on the side of extra-verbose.

Our dataset consists of users and movies; a movie can be rated from 1 star (terrible) to 5 stars (awesome). We’ll represent it with a Rating record type, associating a UserId, MovieId, and Rating:

type UserId = int
type MovieId = int
type Rating = { UserId:UserId; MovieId:MovieId; Rating:int }


To make our life simpler, and to be able to validate whether “it works”, we’ll imagine a world where only 3 types of movies exist, say, Action, Romance and Documentary – and where people have simple tastes: people either love Action and hate the rest, love Romance or hate the rest, or love Documentaries and hate the rest. We’ll assume that we have only 12 movies in our catalog: 0 to 3 are Action, 4 to 7 Romance, and 8 to 11 Documentary.

More...

14. April 2013 12:20

Last Thursday, I gave a talk at the Bay.NET user group in Berkeley, introducing F# to C# developers. First off, I have to thank everybody who came – you guys were great, lots of good questions, nice energy, I had a fantastic time!

My goal was to highlight why I think F# is awesome, and of course this had to include a Type Provider demo, one of the most amazing features of F# 3.0. So I went ahead, and demoed Tomas Petricek’s World Bank Type Provider, and Howard Mansell’s R Type Provider – together. The promise of Type Providers is to enable information-rich programming; in this case, we get immediate access to a wealth of data over the internet, in one line of code, entirely discoverable by IntelliSense in Visual Studio - and we can use all the visualization arsenal of R to see what’s going on. Pretty rad.

Rather than just dump the code, I thought it would be fun to turn that demo into a video. The result is a 7 minutes clip, with only minor editing (a few cuts, and I sped up the video x3 because the main point here isn’t how terrible my typing skills are). I think it’s largely self-explanatory, the only points that are worth commenting upon are:

• I am using a NuGet package for the R Type Provider that doesn’t officially exist yet. I figured a NuGet package would make that Type Provider more usable, and spent my week-end creating it, but haven’t published it yet. Stay tuned!
• The most complex part of the demo is probably R’s syntax from hell. For those of you who don’t know R, it’s a free, open-source statistical package which does amazingly cool things. What you need to know to understand this video is that R is very vector-centric. You can create a vector in R using the syntax myData <- c(1,2,3,4), and combine vectors into what’s called a data frame, essentially a collection of features. The R type provider exposes all R packages and functions through a single static type, aptly named R – so for instance, one can create a R vector from F# by typing let myData = R.c( [|1; 2; 3; 4 |]).

That’s it! Let me know what you think, and if you have comments or questions.

10. February 2013 11:25

And the Journey converting “Machine Learning in Action” from Python to F# continues! Rather than following the order of the book, I decided to skip chapters 8 and 9, dedicated to regression methods (regression is something I spent a bit too much time doing in the past to be excited about it just right now), and go straight to Unsupervised Learning, which begins with the K-means clustering algorithm.

In a nutshell, clustering focuses on the following question: given a set of observations, can the computer figure out a way to classify them into “meaningful groups”? The major difference with Classification methods is that in clustering, the Categories / Groups are initially unknown: it’s the algorithm’s job to figure out sensible ways to group items into Clusters, all by itself (hence the word “unsupervised”).

Chapter 10 covers 2 clustering algorithms, k-means , and bisecting k-means. We’ll discuss only the first one today.

The underlying idea behind the k-means algorithm is to identify k “representative archetypes” (k being a user input), the Centroids. The algorithm proceeds iteratively:

• Starting from k random Centroids,
• Observations are assigned to the closest Centroid, and constitute a Cluster,
• Centroids are updated, by taking the average of their Cluster,
• Until the allocation of Observation to Clusters doesn’t change any more.

When things go well, we end up with k stable Centroids (minimal modification of Centroids do not change the Clusters), and Clusters contain Observations that are similar, because they are all close to the same Centroid (The wikipedia page for the algorithm provides a nice graphical representation).

## F# implementation

The Python implementation proposed in the book is both very procedural and deals with Observations that are vectors. I thought it would be interesting to take a different approach, focused on functions instead. The current implementation is likely to change when I get into bisecting k-means, but should remain similar in spirit. Note also that I have given no focus to performance – this is my take on the easiest thing that would work.

The entire code can be found here on GitHub.

Here is how I approached the problem. First, rather than restricting ourselves to vectors, suppose we want to deal with any generic type. Looking at the pseudo-code above, we need a few functions to implement the algorithm:

• to assign Observations of type ‘a to the closest Centroid ‘a, we need a notion of Distance,
• we need to create an initial collection of k Centroids of type ‘a, given a dataset of ‘as,
• to update the Centroids based on a Cluster of ‘as, we need some aggregation function.

Let’s create these 3 functions:

    // the Distance between 2 observations 'a is a float
// It also better be positive - left to the implementer
type Distance<'a> = 'a -> 'a -> float
// CentroidsFactory, given a dataset,
// should generate n Centroids
type CentroidsFactory<'a> = 'a seq -> int -> 'a seq
// Given a Centroid and observations in a Cluster,
// create an updated Centroid
type ToCentroid<'a> = 'a -> 'a seq -> 'a


We can now define a function which, given a set of Centroids, will return the index of the closest Centroid to an Observation, as well as the distance from the Centroid to the Observation:

    // Returns the index of and distance to the
// Centroid closest to observation
let closest (dist: Distance<'a>) centroids (obs: 'a) =
centroids
|> Seq.mapi (fun i c -> (i, dist c obs))
|> Seq.minBy (fun (i, d) -> d)


Finally, we’ll go for the laziest possible way to generate k initial Centroids, by picking up k random observations from our dataset:

    // Picks k random observations as initial centroids
// (this is very lazy, even tolerates duplicates)
let randomCentroids<'a> (rng: System.Random)
(sample: 'a seq)
k =
let size = Seq.length sample
seq { for i in 1 .. k do
let pick = Seq.nth (rng.Next(size)) sample
yield pick }


More...