## Archive for the ‘**Data Science**’ Category

## Tackling the John Smith Problem – deduplicating data via fuzzy matching in R

Last week I attended a CRM & data user group meeting for not-for-profits (NFPs), organized by my friend Yael Wasserman from Mission Australia. Following a presentation from a vendor, we broke up into groups and discussed common data quality issues that NFPs (and dare I say most other organisations) face. Number one on the list was the vexing issue of duplicate constituent (donor) records – henceforth referred to as dupes. I like to call this the *John Smith Problem* as it is likely that a typical customer database in a country with a large Anglo population is likely to have a fair number of records for customers with that name. The problem is tricky because one has to identify John Smiths who appear to be distinct in the database but are actually the same person, while also ensuring that one does not inadvertently merge two distinct John Smiths.

The John Smith problem is particularly acute for NFPs as much of their customer data comes in either via manual data entry or bulk loads with less than optimal validation. To be sure, all the NFPs represented at the meeting have some level of validation on both modes of entry, but all participants admitted that dupes tend to sneak in nonetheless…and at volumes that merit serious attention. Yael and his team have had some success in cracking the dupe problem using SQL-based matching of a combination of fields such as first name, last name and address or first name, last name and phone number and so on. However, as he pointed out, this method is limited because:

- It does not allow for typos and misspellings.
- Matching on too few fields runs the risk of false positives – i.e. labelling non-dupes as dupes.

The problems arise because SQL-based matching requires one to pre-specify match patterns. The solution is straightforward: use fuzzy matching instead. The idea behind fuzzy matching is simple: allow for inexact matches, assigning each match a *similarity score* ranging from 0 to 1 with 0 being complete dissimilarity and 1 being a perfect match. My primary objective in this article is to show how one can make headway with the John Smith problem using the fuzzy matching capabilities available in R.

### A bit about fuzzy matching

Before getting down to fuzzy matching, it is worth a brief introduction on how it works. The basic idea is simple: one has to generalise the notion of a match from a binary *“match” / “no match”* to allow for partial matching. To do this, we need to introduce the notion of an edit distance, which is essentially the *minimum number of operations required to transform one string into another*. For example, the edit distance between the strings *boy* and *bay* is 1: there’s only one edit required to transform one string to the other. The Levenshtein distance is the most commonly used edit distance. It is essentially, “*the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.”*

A variant called the Damerau-Levenshtein distance, which additionally allows for the transposition of two adjacent characters (counted as one operation, not two) is found to be more useful in practice. We’ll use an implementation of this called the *optimal string alignment* (osa) distance. If you’re interested in finding out more about osa, check out the Damerau-Levenshtein article linked to earlier in this paragraph.

Since longer strings will potentially have larger numeric distances between them, it makes sense to normalise the distance to a value lying between 0 and 1. We’ll do this by dividing the calculated osa distance by the length of the larger of the two strings . Yes, this is crude but, as you will see, it works reasonably well. The resulting number is a normalised measure of the dissimilarity between the two strings. To get a similarity measure we simply subtract the dissimilarity from 1. So, a normalised dissimilarity of 1 translates to similarity score of 0 – i.e. the strings are perfectly *dissimilar*. I hope I’m not belabouring the point; I just want to make sure it is perfectly clear before going on.

### Preparation

In what follows, I assume you have R and RStudio installed. If not, you can access the software here and here for Windows and here for Macs; installation for both products is usually quite straightforward.

You may also want to download the Excel file **many_john_smiths** which contains records for ten fictitious John Smiths. At this point I should affirm that as far as the dataset is concerned, *a**ny resemblance to actual John Smiths, living or dead, is purely coincidental! *Once you have downloaded the file you will want to open it in Excel and examine the records and save it as a csv file in your R working directory (or any other convenient place) for processing in R.

As an aside, if you have access to a database, you may also want to load the file into a table called **many_john_smiths** and run the following dupe-detecting SQL statement:

select * from many_john_smiths t1

where exists

(select 'x' from many_john_smiths t2

where

t1.FirstName=t2.FirstName

and

t1.LastName=t2.LastName

and

t1.AddressPostcode=t2.AddressPostcode

and

t1.CustomerID <> t2.CustomerID)

You may also want to try matching on other column combinations such as *First/Last Name and AddressLine1* or F*irst/Last Name and AddressSuburb* for example. The limitations of column-based exact matching will be evident immediately. Indeed, I have deliberately designed the records to highlight some of the issues associated with dirty data: misspellings, typos, misheard names over the phone etc. A quick perusal of the records will show that there are *probably* two distinct John Smiths in the list. The problem is to quantify this observation. We do that next.

### Tackling the John Smith problem using R

We’ll use the following libraries: stringdist and stringi . The first library, stringdist, contains a bunch of string distance functions, we’ll use stringdistmatrix() which returns a matrix of pairwise string distances (osa by default) when passed a vector of strings, and stringi has a number of string utilities from which we’ll use str_length(), which returns the length of string.

OK, so on to the code. The first step is to load the required libraries:

`#load libraries`

library("stringdist")

library("stringr")

We then read in the data, ensuring that we override the annoying default behaviour of R, which is to convert strings to categorical variables – we want strings to remain strings!

`#read data, taking care to ensure that strings remain strings`

df <- read.csv("many_john_smiths.csv",stringsAsFactors = F)

#examine dataframe

str(df)

The output from str(df) (not shown) indicates that all columns barring *CustomerID* are indeed strings (i.e. type=character).

The next step is to find the length of each row:

#find length of string formed by each row (excluding title)

rowlen <- str_length(paste0(df$FirstName,df$LastName,df$AddressLine1,

df$AddressPostcode,df$AddressSuburb,df$Phone))

#examine row lengths

rowlen

> [1] 41 43 39 42 28 41 42 42 42 43

Note that I have excluded the *Title* column as I did not think it was relevant to determining duplicates.

Next we find the distance between every pair of records in the dataset. We’ll use the stringdistmatrix()function mentioned earlier:

#stringdistmatrix - finds pairwise osa distance between every pair of elements in a

#character vector

d <- stringdistmatrix(paste0(df$FirstName,df$LastName,df$AddressLine1,

df$AddressPostcode,df$AddressSuburb,df$Phone))

d

1 2 3 4 5 6 7 8 9

2 7

3 10 13

4 15 21 24

5 19 26 26 15

6 22 21 28 12 18

7 20 23 26 9 21 14

8 10 13 17 20 23 25 22

9 19 22 19 21 24 29 23 22

10 17 22 25 13 22 19 16 22 24

stringdistmatrix() returns an object of type dist (distance), which is essentially a vector of pairwise distances.

For reasons that will become clear later, it is convenient to normalise the distance – i.e. scale it to a number that lies between 0 and 1. We’ll do this by dividing the distance between two strings by the length of the longer string. We’ll use the nifty base R function combn() to compute the maximum length for every pair of strings:

#find the length of the longer of two strings in each pair

pwmax <- combn(rowlen,2,max,simplify = T)

The first argument is the vector from which combinations are to be generated, the second is the group size (2, since we want pairs) and the third argument indicates whether or not the result should be returned as an array (simplify=T) or list (simplify=F). The returned object, pwmax, is a one-dimensional array containing the pairwise maximum lengths. This has the same length and is organised in the same way as the object d returned by stringdistmatrix() (check that!). Therefore, to normalise d we simply divide it by pwmax

#normalised distance

dist_norm <- d/pwmax

The normalised distance lies between 0 and 1 (check this!) so we can define similarity as 1 minus distance:

#similarity = 1 - distance

similarity <- round(1-dist_norm,2)

sim_matrix <- as.matrix(similarity)

sim_matrix

1 2 3 4 5 6 7 8 9 10

1 0.00 0.84 0.76 0.64 0.54 0.46 0.52 0.76 0.55 0.60

2 0.84 0.00 0.70 0.51 0.40 0.51 0.47 0.70 0.49 0.49

3 0.76 0.70 0.00 0.43 0.33 0.32 0.38 0.60 0.55 0.42

4 0.64 0.51 0.43 0.00 0.64 0.71 0.79 0.52 0.50 0.70

5 0.54 0.40 0.33 0.64 0.00 0.56 0.50 0.45 0.43 0.49

6 0.46 0.51 0.32 0.71 0.56 0.00 0.67 0.40 0.31 0.56

7 0.52 0.47 0.38 0.79 0.50 0.67 0.00 0.48 0.45 0.63

8 0.76 0.70 0.60 0.52 0.45 0.40 0.48 0.00 0.48 0.49

9 0.55 0.49 0.55 0.50 0.43 0.31 0.45 0.48 0.00 0.44

10 0.60 0.49 0.42 0.70 0.49 0.56 0.63 0.49 0.44 0.00

The diagonal entries are 0, but that doesn’t matter because we know that every string is perfectly similar to itself! Apart from that, the similarity matrix looks quite reasonable: you can, for example, see that records 1 and 2 (similarity score=0.84) are quite similar while records 1 and 6 are quite dissimilar (similarity score=0.46). Now let’s extract some results more systematically. We’ll do this by printing out the top 5 non-diagonal similarity scores and the associated records for each of them. This needs a bit of work. To start with, we note that the similarity matrix (like the distance matrix) is symmetric so we’ll convert it into an upper triangular matrix to avoid double counting. We’ll also set the diagonal entries to 0 to avoid comparing a record with itself:

#convert to upper triangular to prevent double counting

sim_matrix[lower.tri(sim_matrix)] <- 0

#set diagonals to zero to avoid comparing row to itself

diag(sim_matrix) <- 0

Next we create a function that returns the n largest similarity scores and their associated row and column number – we’ll need the latter to identify the pair of records that are associated with each score:

#adapted from:

#https://stackoverflow.com/questions/32544566/find-the-largest-values-on-a-matrix-in-r

nlargest <- function(m, n) {

res <- order(m, decreasing = T)[seq_len(n)];

pos <- arrayInd(res, dim(m), useNames = TRUE);

list(values = m[res],

position = pos)

}

The function takes two arguments: a matrix m and a number n indicating the top n scores to be returned. Let’s set this number to 5 – i.e. we want the top 5 scores and the associated record indexes. We’ll store the output of nlargest in the variable sim_list:

top_n <- 5

sim_list <- nlargest(sim_matrix,top_n)

Finally, we loop through sim_list printing out the scores and associated records as we go along:

for (i in 1:top_n){

rec <- as.character(df[sim_list$position[i],])

sim_rec <- as.character(df[sim_list$position[i+top_n],])

cat("score: ",sim_list$values[i],"\n")

cat("record 1: ",rec,"\n")

cat ("record 2: ",sim_rec,"\n\n")

}

score: 0.84

record 1: 1 John Smith Mr 12 Acadia Rd Burnton 9671 1234 5678

record 2: 2 Jhon Smith Mr 12 Arcadia Road Bernton 967 1233 5678

score: 0.79

record 1: 4 John Smith Mr 13 Kynaston Rd Burnton 9671 34561234

record 2: 7 Jon Smith Mr. 13 Kinaston Rd Barnston 9761 36451223

score: 0.76

record 1: 1 John Smith Mr 12 Acadia Rd Burnton 9671 1234 5678

record 2: 3 J Smith Mr. 12 Acadia Ave Burnton 867`1 1233 567

score: 0.76

record 1: 1 John Smith Mr 12 Acadia Rd Burnton 9671 1234 5678

record 2: 8 John Smith Dr 12 Aracadia St Brenton 9761 12345666

score: 0.71

record 1: 4 John Smith Mr 13 Kynaston Rd Burnton 9671 34561234

record 2: 6 John S Dr. 12 Kinaston Road Bernton 9677 34561223

As you can see, the method correctly identifies close matches: there appear to be 2 distinct records (1 and 4) – and possibly more, depending on where one sets the similarity threshold. I’ll leave you to explore this further on your own.

### The John Smith problem in real life

As a proof of concept, I ran the following SQL on a real CRM database hosted on SQL Server:

select

FirstName+LastName,

count(*)

from

TableName

group by

FirstName+LastName

having

count(*)>100

order by

count(*) desc

I was gratified to note that John Smith did indeed come up tops – well over 200 records. I suspected there were a few duplicates lurking within, so I extracted the records and ran the above R code (with a few minor changes). I found there indeed were some duplicates! I also observed that the code ran with no noticeable degradation despite the dataset having well over 10 times the number of records used in the toy example above. I have not run it for larger datasets yet, but I suspect one will run into memory issues when the number of records gets into the thousands. Nevertheless, based on my experimentation thus far, this method appears viable for small datasets.

The problem of deduplicating large datasets is left as an exercise for motivated readers 😛

### Wrapping up

Often organisations will turn to specialist consultancies to fix data quality issues only to find that their work, besides being quite pricey, comes with a lot of caveats and cosmetic fixes that do not address the problem fully. Given this, there is a case to be made for doing as much of the exploratory groundwork as one can so that one gets a good idea of what can be done and what cannot. At the very least, one will then be able to keep one’s consultants on their toes. In my experience, the John Smith problem ranks right up there in the list of data quality issues that NFPs and many other organisations face. This article is intended as a starting point to address this issue using an easily available and cost effective technology.

Finally, I should reiterate that the approach discussed here is just one of many possible and is neither optimal nor efficient. Nevertheless, it works quite well on small datasets, and is therefore offered here as a starting point for your own attempts at tackling the problem. If you come up with something better – as I am sure you can – I’d greatly appreciate your letting me know via the contact page on this blog or better yet, a comment.

**Acknowledgements:**

I’m indebted to Homan Zhao and Sree Acharath for helpful conversations on fuzzy matching. I’m also grateful to all those who attended the NFP CRM and Data User Group meetup that was held earlier this month – the discussions at that meeting inspired this piece.

## An intuitive introduction to support vector machines using R – Part 1

About a year ago, I wrote a piece on support vector machines as a part of my *gentle introduction to data science R* series. So it is perhaps appropriate to begin this piece with a few words about my motivations for writing yet another article on the topic.

Late last year, a curriculum lead at DataCamp got in touch to ask whether I’d be interested in developing a course on SVMs for them.

My answer was, obviously, an enthusiastic “Yes!”

Instead of rehashing what I had done in my previous article, I thought it would be interesting to try an approach that focuses on building an intuition for how the algorithm works using examples of increasing complexity, supported by visualisation rather than math. This post is the first part of a two-part series based on this approach.

The article builds up some basic intuitions about support vector machines (abbreviated henceforth as SVM) and then focuses on linearly separable problems. Part 2 (to be released at a future date) will deal with radially separable and more complex data sets. The focus throughout is on developing an understanding *what* the algorithm does rather than the technical details of how it does it.

Prerequisites for this series are a basic knowledge of R and some familiarity with the *ggplot* package. However, even if you don’t have the latter, you should be able to follow much of what I cover so I encourage you to press on regardless.

<advertisement> if you have a DataCamp account, you may want to check out my course on support vector machines using R. Chapters 1 and 2 of the course closely follow the path I take in this article. </advertisement>

### A one dimensional example

A soft drink manufacturer has two brands of their flagship product: *Choke* (sugar content of 11g/100ml) and *Choke-R* (sugar content 8g/100 ml). The actual sugar content can vary quite a bit in practice so it can sometimes be hard to figure out the brand given the sugar content. Given sugar content data for 25 samples taken randomly from both populations (see file sugar_content.xls), our task is to come up with a decision rule for determining the brand.

Since this is one-variable problem, the simplest way to discern if the samples fall into distinct groups is through visualisation. Here’s one way to do this using *ggplot*:

…and here’s the resulting plot:

Note that we’ve simulated a one-dimensional plot by setting all the y values to 0.

From the plot, it is evident that the samples fall into distinct groups: low sugar content, bounded above by the 8.8 g/100ml sample and high sugar content, bounded below by the 10 g/100ml sample.

Clearly, any point that lies between the two points is an acceptable *decision boundary.* We could, for example, pick 9.1g/100ml and 9.7g/100ml. Here’s the R code with those points added in. Note that we’ve made the points a bit bigger and coloured them red to distinguish them from the sample points.

label=d_bounds$sep, size=2.5,

vjust=2, hjust=0.5, colour=”red”)

And here’s the plot:

Now, a bit about the decision rule. Say we pick the first point as the decision boundary, the decision rule would be:

Say we pick 9.1 as the decision boundary, our classifier (in R) would be:

The other one is left for you as an exercise.

Now, it is pretty clear that although either these points define an acceptable decision boundary, neither of them are the best. Let’s try to formalise our intuitive notion as to why this is so.

The *margin* is the distance between the points in both classes that are closest to the decision boundary. In case at hand, the margin is 1.2 g/100ml, which is the difference between the two extreme points at 8.8 g/100ml (Choke-R) and 10 g/100ml (Choke). It should be clear that the best separator is the one that lies halfway between the two extreme points. This is called the *maximum margin separator*. The maximum margin separator in the case at hand is simply the average of the two extreme points:

geom_point(data=mm_sep,aes(x=mm_sep$sep, y=c(0)), colour=”blue”, size=4)

And here’s the plot:

We are dealing with a one dimensional problem here so the decision boundary is a point. In a moment we will generalise this to a two dimensional case in which the boundary is a straight line.

Let’s close this section with some general points.

Remember this is a sample not the entire population, so it is quite possible (indeed likely) that there will be as yet unseen samples of Choke-R and Choke that have a sugar content greater than 8.8 and less than 10 respectively. So, the best classifier is one that lies at the greatest possible distance from both classes. The maximum margin separator is that classifier.

This toy example serves to illustrate the main aim of SVMs, which is to find an optimal separation boundary in the sense described here. However, doing this for real life problems is not so simple because life is not one dimensional. In the remainder of this article and its yet-to-be-written sequel, we will work through examples of increasing complexity so as to develop a good understanding of how SVMs work in addition to practical experience with using the popular SVM implementation in R.

<Advertisement> Again, for those of you who have DataCamp premium accounts, here is a course that covers pretty much the entire territory of this two part series. </Advertisement>

### Linearly separable case

The next level of complexity is a two dimensional case (2 predictors) in which the classes are separated by a straight line. We’ll create such a dataset next.

Let’s begin by generating 200 points with attributes x1 and x2, randomly distributed between 0 and 1. Here’s the R code:

Let’s visualise the generated data using a scatter plot:

And here’s the plot

Now let’s classify the points that lie above the line *x1=x2* as belonging to the class +1 and those that lie below it as belonging to class -1 (the class values are arbitrary choices, I could have chosen them to be anything at all). Here’s the R code:

Let’s modify the plot in Figure 4, colouring the points classified as +1n blue and those classified -1 red. For good measure, let’s also add in the decision boundary. Here’s the R code:

Note that the parameters in *geom_abline()* are derived from the fact that the line* x1=x2* has slope 1 and y intercept 0.

Here’s the resulting plot:

Next let’s introduce a margin in the dataset. To do this, we need to exclude points that lie within a specified distance of the boundary. A simple way to approximate this is to exclude points that have *x1* and *x2* values that differ by less a pre-specified value, *delta*. Here’s the code to do this with *delta* set to 0.05 units.

The check on the number of datapoints tells us that a number of points have been excluded.

Running the *previous* *ggplot* code block yields the following plot which clearly shows the reduced dataset with the depopulated region near the decision boundary:

Let’s add the margin boundaries to the plot. We know that these are parallel to the decision boundary and lie delta units on either side of it. In other words, the margin boundaries have slope=1 and y intercepts *delta* and –*delta*. Here’s the *ggplot* code:

And here’s the plot with the margins:

OK, so we have constructed a dataset that is *linearly separable*, which is just a short code for saying that the classes can be separated by a straight line. Further, the dataset has a margin, i.e. there is a “gap” so to speak, between the classes. Let’s save the dataset so that we can use it in the next section where we’ll take a first look at the *svm()* function in the *e1071* package.

That done, we can now move on to…

### Linear SVMs

Let’s begin by reading in the datafile we created in the previous section:

We then split the data into training and test sets using an 80/20 random split. There are many ways to do this. Here’s one:

The next step is to build the an SVM classifier model. We will do this using the *svm() f*unction which is available in the *e1071* package. The *svm()* function has a range of parameters. I explain some of the key ones below, in particular, the following parameters: *type*, *cost*, *kernel* and *scale*. It is recommended to have a browse of the documentation for more details.

The *type* parameter specifies the algorithm to be invoked by the function. The algorithm is capable of doing both classification and regression. We’ll focus on classification in this article. Note that there are two types of classification algorithms, nu and C classification. They essentially differ in the way that they penalise margin and boundary violations, but can be shown to lead to equivalent results. We will stick with C classification as it is more commonly used. The “C” refers to the cost which we discuss next.

The *cost* parameter specifies the penalty to be applied for boundary violations. This parameter can vary from 0 to infinity (in practice a large number compared to 0, say 10^6 or 10^8). We will explore the effect of varying *cost* later in this piece. To begin with, however, we will leave it at its default value of 1.

The *kernel* parameter specifies the kind of function to be used to construct the decision boundary. The options are linear, polynomial and radial. In this article we’ll focus on linear kernels as we know the decision boundary is a straight line.

The *scale* parameter is a Boolean that tells the algorithm whether or not the datapoints should be scaled to have zero mean and unit variance (i.e. shifted by the mean and scaled by the standard deviation). Scaling is generally good practice to avoid undue influence of attributes that have unduly large numeric values. However, in this case we will avoid scaling as we know the attributes are bounded and (more important) we would like to plot the boundary obtained from the algorithm manually.

Building the model is a simple one-line call, setting appropriate values for the parameters:

We expect a linear model to perform well here since the dataset it is linear by construction. Let’s confirm this by calculating training and test accuracy. Here’s the code:

The perfect accuracies confirm our expectation. However, accuracies by themselves are misleading because the story is somewhat more nuanced. To understand why, let’s plot the predicted decision boundary and margins using *ggplot*. To do this, we have to first extract information regarding these from the svm model object. One can obtain summary information for the model by typing in the model name like so:

kernel = “linear”, scale = FALSE)

Which outputs the following: the function *call*, SVM *type*, *kernel* and *cost* (which is set to its default). In case you are wondering about *gamma, * although it’s set to 0.5 here, it plays no role in linear SVMs. We’ll say more about it in the sequel to this article in which we’ll cover more complex kernels. More interesting are the *support vectors*. In a nutshell, these are training dataset points that *specify the location of the decision boundary*. We can develop a better understanding of their role by visualising them. To do this, we need to know their coordinates and indices (position within the dataset). This information is stored in the SVM model object. Specifically, the *index* element of *svm_model* contains the indices of the training dataset points that are support vectors and the *SV* element lists the coordinates of these points. The following R code lists these explicitly (Note that I’ve not shown the outputs in the code snippet below):

Let’s use the indices to visualise these points in the training dataset. Here’s the ggplot code to do that:

And here is the plot:

We now see that the support vectors are clustered around the boundary and, in a sense, serve to define it. We will see this more clearly by plotting the predicted decision boundary. To do this, we need its slope and intercept. These aren’t available directly available in the *svm_model*, but they can be extracted from the *coefs*, *SV* and *rho* elements of the object.

The first step is to use *coefs* and the support vectors to build the what’s called the *weight vector*. The *weight vector* is given by the product of the *coefs* matrix with the matrix containing the SVs. Note that the fact that only the support vectors play a role in defining the boundary is consistent with our expectation that the boundary should be fully specified by them. Indeed, this is often touted as a feature of SVMs in that it is one of the few classifiers that depends on only a small subset of the training data, i.e. the datapoints closest to the boundary rather than the entire dataset.

Once we have the weight vector, we can calculate the slope and intercept of the predicted decision boundary as follows:

Note that the slope and intercept are quite different from the correct values of 1 and 0 (reminder: the actual decision boundary is the line *x1=x2* by construction). We’ll see how to improve on this shortly, but before we do that, let’s plot the decision boundary using the slope and intercept we have just calculated. Here’s the code:

And here’s the augmented plot:

The plot clearly shows how the support vectors “support” the boundary – indeed, if one draws line segments from each of the points to the boundary in such a way that the intersect the boundary at right angles, the lines can be thought of as “holding the boundary in place”. Hence the term *support vector*.

This is a good time to mention that the *e1071* library provides a built-in plot method for *svm* function. This is invoked as follows:

The svm *plot *function takes a formula specifying the plane on which the boundary is to be plotted. This is not necessary here as we have only two predictors (x1 and x2) which automatically define a plane.

Here is the plot generated by the above code:

Note that the axes are switched (x1 is on the y axis). Aside from that, the plot is reassuringly similar to our *ggplot* version in Figure 9. Also note that that the support vectors are marked by “x”. Unfortunately the built in function does not display the margin boundaries, but this is something we can easily add to our home-brewed plot. Here’s how. We know that the margin boundaries are parallel to the decision boundary, so all we need to find out is their intercept. It turns out that the intercepts are offset by an amount *1/w[2]* units on either side of the decision boundary. With that information in hand we can now write the the code to add in the margins to the plot shown in Figure 9. Here it is:

geom_abline(slope=slope_1,intercept = intercept_1+1/w[2], linetype=”dashed”)

And here is the plot with the margins added in:

Note that the predicted margins are much wider than the actual ones (compare with Figure 7). As a consequence, many of the support vectors lie within the predicted margin – that is, they violate it. The upshot of the wide margin is that the decision boundary is not tightly specified. This is why we get a significant difference between the slope and intercept of predicted decision boundary and the actual one. We can sharpen the boundary by narrowing the margin. How do we do this? We make margin violations more expensive by increasing the *cost*. Let’s see this margin-narrowing effect in action by building a model with *cost = *100 on the same training dataset as before. Here is the code:

I’ll leave you to calculate the training and test accuracies (as one might expect, these will be perfect).

Let’s inspect the *cost=100* model:

kernel = “linear”,cost=100, scale = FALSE)

The number of support vectors is reduced from 55 to 6! We can plot these and the boundary / margin lines using *ggplot* as before. The code is identical to the previous case (see code block preceding Figure 8). If you run it, you will get the plot shown in Figure 12.

Since the boundary is more tightly specified, we would expect the slope and intercept of the predicted boundary to be considerably closer to their actual values of 1 and 0 respectively (as compared to the default cost case). Let’s confirm that this is so by calculating the slope and intercept as we did in the code snippets preceding Figure 9. Here’s the code:

Which nicely confirms our expectation.

The decision boundary and margins for the high cost case can also be plotted with the code shown earlier. Her it is for completeness:

geom_abline(slope=slope_100,intercept = intercept_100+1/w[2], linetype=”dashed”)

And here’s the plot:

SVMs that allow margin violations are called *soft margin classifiers* and those that do not are called *hard*. In this case, the hard margin classifier does a better job because it specifies the boundary more accurately than its soft counterpart. However, this does not mean that hard margin classifier are to be preferred over soft ones in all situations. Indeed, in real life, where we usually do not know the shape of the decision boundary upfront, soft margin classifiers can allow for a greater degree of uncertainty in the decision boundary thus improving generalizability of the classifier.

OK, so now we have a good feel for what the SVM algorithm does in the linearly separable case. We will round out this article by looking at a real world dataset that fortuitously turns out to be almost linearly separable: the famous (notorious?) iris dataset. It is instructive to look at this dataset because it serves to illustrate another feature of the *e1071* SVM algorithm – its capability to handle classification problems that have more than 2 classes.

### A multiclass problem

The iris dataset is well-known in the machine learning community as it features in many introductory courses and tutorials. It consists of 150 observations of 3 *species* of the iris flower – *setosa*, *versicolor* and *virginica*. Each observation consists of numerical values for 4 independent variables (predictors): petal length, petal width, sepal length and sepal width. The dataset is available in a standard installation of R as a built in dataset. Let’s read it in and examine its structure:

Now, as it turns out, petal length and petal width are the key determinants of species. So let’s create a scatterplot of the datapoints as a function of these two variables (i.e. project each data point on the petal length-petal width plane). We will also distinguish between species using different colour. Here’s the ggplot code to do this:

And here’s the plot:

On this plane we see a clear linear boundary between *setosa* and the other two species, *versicolor* and *virginica*. The boundary between the latter two is almost linear. Since there are four predictors, one would have to plot the other combinations to get a better feel for the data. I’ll leave this as an exercise for you and move on with the assumption that the data is nearly linearly separable. If the assumption is grossly incorrect, a linear SVM will not work well.

Up until now, we have discussed binary classification problem, i.e. those in which the predicted variable can take on only two values. In this case, however, the predicted variable, *Species*, can take on 3 values (setosa, versicolor and virginica). This brings up the question as to how the algorithm deals multiclass classification problems – i.e those involving datasets with more than two classes. The SVM algorithm does this using a *one-against-one *classification strategy. Here’s how it works:

- Divide the dataset (assumed to have N classes) into N(N-1)/2 datasets that have two classes each.
- Solve the binary classification problem for each of these subsets
- Use a simple voting mechanism to assign a class to each data point.

Basically, each data point is assigned the most frequent classification it receives from all the binary classification problems it figures in.

With that said, let’s get on with building the classifier. As before, we begin by splitting the data into training and test sets using an 80/20 random split. Here is the code to do this:

Then we build the model (default cost) and examine it:

The main thing to note is that the function call is identical to the binary classification case. We get some basic information about the model by typing in the model name as before:

kernel = “linear”)

And the train and test accuracies are computed in the usual way:

This looks good, but is potentially misleading because it is for a particular train/test split. Remember, in this case, unlike the earlier example, we do not know the shape of the actual decision boundary. So, to get a robust measure of accuracy, we should calculate the average test accuracy over a number of train/test partitions. Here’s some code to do that:

Which is not too bad at all, indicating that the dataset is indeed nearly linearly separable. If you try different values of *cost* you will see that it does not make much difference to the average accuracy.

This is a good note to close this piece on. Those who have access to DataCamp premium courses will find that the content above is covered in chapters 1 and 2 of the course on support vector machines in R. The next article in this two-part series will cover chapters 3 and 4.

## Summarising

My main objective in this article was to help develop an intuition for how SVMs work in simple cases. We illustrated the basic principles and terminology with a simple 1 dimensional example and then worked our way to linearly separable binary classification problems with multiple predictors. We saw how the latter can be solved using a popular svm implementation available in R. We also saw that the algorithm can handle multiclass problems. All through, we used visualisations to see what the algorithm does and how the key parameters affect the decision boundary and margins.

In the next part (yet to be written) we will see how SVMs can be generalised to deal with complex, nonlinear decision boundaries. In essence, the use a mathematical trick to “linearise” these boundaries. We’ll delve into details of this trick in an intuitive, visual way as we have done here.

Many thanks for reading!

## A gentle introduction to data visualisation using R

Data science students often focus on machine learning algorithms, overlooking some of the more routine but important skills of the profession. I’ve lost count of the number of times I have advised students working on projects for industry clients to curb their keenness to code and work on understanding the data first. This is important because, as people (ought to) know, data doesn’t speak for itself, it has to be given a voice; and as data-scarred professionals know from hard-earned experience, one of the best ways to do this is through visualisation.

Data visualisation is sometimes (often?) approached as a bag of tricks to be learnt individually, with no little or no reference to any underlying principles. Reading Hadley Wickham’s paper on the grammar of graphics was an epiphany; it showed me how different types of graphics can be constructed in a consistent way using common elements. Among other things, the grammar makes visualisation a logical affair rather than a set of tricks. This post is a brief – and hopefully logical – introduction to visualisation using ggplot2, Wickham’s implementation of a grammar of graphics.

In keeping with the practical bent of this series we’ll focus on worked examples, illustrating elements of the grammar as we go along. We’ll first briefly describe the elements of the grammar and then show how these are used to build different types of visualisations.

### A grammar of graphics

Most visualisations are constructed from common elements that are pieced together in prescribed ways. The elements can be grouped into the following categories:

**Data**– this is obvious, without data there is no story to tell and definitely no plot!**Mappings**– these are correspondences between data and display elements such as spatial location, shape or colour. Mappings are referred to as*aesthetics*in Wickham’s grammar.**Scales**– these are transformations (conversions) of data values to numbers that can be displayed on-screen. There should be one scale per mapping. ggplot typically does the scaling transparently, without users having to worry about it. One situation in which you might need to mess with default scales is when you want to zoom in on a particular range of values. We’ll see an example or two of this later in this article.**Geometric objects**– these specify the geometry of the visualisation. For example, in ggplot2 a scatter plot is specified via a*point*geometry whereas a fitting curve is represented by a*smooth*geometry. ggplot2 has a range of geometries available of which we will illustrate just a few.**Coordinate system –**this specifies the system used to position data points on the graphic. Examples of coordinate systems are Cartesian and polar. We’ll deal with Cartesian systems in this tutorial. See this post for a nice illustration of how one can use polar plots creatively.**Facets**– a facet specifies how data can be split over multiple plots to improve clarity. We’ll look at this briefly towards the end of this article.

The basic idea of a *layered* grammar of graphics is that each of these elements can be combined – literally added layer by layer – to achieve a desired visual result. Exactly how this is done will become clear as we work through some examples. So without further ado, let’s get to it.

### Hatching (gg)plots

In what follows we’ll use the NSW Government Schools dataset, made available via the state government’s open data initiative. The data is in csv format. If you cannot access the original dataset from the aforementioned link, you can download an Excel file with the data **here** (remember to save it as a csv before running the code!).

The first task – assuming that you have a working R/RStudio environment – is to load the data into R. To keep things simple we’ll delete a number of columns (as shown in the code) and keep only rows that are complete, i.e. those that have no missing values. Here’s the code:

A note regarding the last line of code above, a couple of schools have “np” entered for the* student_number* variable. These are coerced to NA in the numeric conversion. The last line removes these two schools from the dataset.

Apart from *student numbers* and location data, we have retained *level of schooling* (primary, secondary etc.) and* ICSEA ranking*. The location information includes attributes such as *suburb*, *postcode*, *region*, *remoteness* as well as *latitude* and *longitude*. We’ll use only *remoteness* in this post.

The first thing that caught my eye in the data was was the ICSEA ranking. Before going any further, I should mention that the Australian Curriculum Assessment and Reporting Authority (the organisation responsible for developing the ICSEA system) emphasises that the score is *not* a school ranking, but a measure of *socio-educational advantage* of the student population in a school. Among other things, this is related to family background and geographic location. The average ICSEA score is set at an average of 1000, which can be used as a reference level.

I thought a natural first step would be to see how ICSEA varies as a function of the other variables in the dataset such as *student number*s and *location* (*remoteness*, for example). To begin with, let’s plot ICSEA rank as a function of student number. As it is our first plot, let’s take it step by step to understand how the layered grammar works. Here we go:

This displays a blank plot because we have not specified a *mapping* and *geometry *to go with the data. To get a plot we need to specify both. Let’s start with a scatterplot, which is specified via a point geometry. Within the geometry function, variables are mapped to visual properties of the using aesthetic mappings. Here’s the code:

The resulting plot is shown in Figure 1.

At first sight there are two points that stand out: 1) there are fewer number of large schools, which we’ll look into in more detail later and 2) larger schools seem to have a higher ICSEA score on average. To dig a little deeper into the latter, let’s add a linear trend line. We do that by adding another layer (geometry) to the scatterplot like so:

The result is shown in Figure 2.

The *lm* method does a linear regression on the data. The shaded area around the line is the 95% confidence level of the regression line (i.e that it is 95% certain that the true regression line lies in the shaded region). Note that *geom_smooth * provides a range of smoothing functions including generalised linear and local regression (loess) models.

You may have noted that we’ve specified the aesthetic mappings in both *geom_point* and *geom_smooth*. To avoid this duplication, we can simply specify the mapping, once in the top level ggplot call (the first layer) like so:

geom_point()+

geom_smooth(method=”lm”)

From Figure 2, one can see a clear positive correlation between student numbers and ICSEA scores, let’s zoom in around the average value (1000) to see this more clearly…

The *coord_cartesian* function is used to zoom the plot to without changing any other settings. The result is shown in Figure 3.

To make things clearer, let’s add a reference line at the average:

The result, shown in Figure 4, indicates quite clearly that larger schools tend to have higher ICSEA scores. That said, there is a twist in the tale which we’ll come to a bit later.

As a side note, you would use *geom_vline* to zoom in on a specific range of x values and *geom_abline* to add a reference line with a specified slope and intercept. See this article on ggplot reference lines for more.

OK, now that we have seen how *ICSEA scores* vary with *student number*s let’s switch tack and incorporate another variable in the mix. An obvious one is *remoteness*. Let’s do a scatterplot as in Figure 1, but now colouring each point according to its remoteness value. This is done using the colour aesthetic as shown below:

geom_point()

The resulting plot is shown in Figure 5.

Aha, a couple of things become apparent. First up, large schools tend to be in metro areas, which makes good sense. Secondly, it appears that metro area schools have a distinct socio-educational advantage over regional and remote area schools. Let’s add trendlines by remoteness category as well to confirm that this is indeed so:

The plot, which is shown in Figure 6, indicates clearly that ICSEA scores decrease on the average as we move away from metro areas.

Moreover, larger schools metropolitan areas tend to have higher than average scores (above 1000), regional areas tend to have lower than average scores overall, with remote areas being markedly more disadvantaged than both metro and regional areas. This is no surprise, but the visualisations show just how stark the differences are.

It is also interesting that, in contrast to metro and (to some extent) regional areas, there *negative correlation* between student numbers and scores for remote schools. One can also use local regression to get a better picture of how ICSEA varies with student numbers and remoteness. To do this, we simply use the *loess* method instead of *lm:*

geom_point() + geom_hline(yintercept=1000) + geom_smooth(method=”loess”)

The result, shown in Figure 7, has a number of interesting features that would have been worth pursuing further were we analysing this dataset in a real life project. For example, why do small schools tend to have lower than benchmark scores?

From even a casual look at figures 6 and 7, it is clear that the confidence intervals for remote areas are huge. This suggests that the number of datapoints for these regions are a) small and b) very scattered. Let’s quantify the number by getting counts using the *table* function (I know, we could plot this too…and we will do so a little later). We’ll also transpose the results using *data.frame* to make them more readable:

The number of datapoints for remote regions is *much* less than those for metro and regional areas. Let’s repeat the loess plot with only the two remote regions. Here’s the code:

geom_point() + geom_hline(yintercept=1000) + geom_smooth(method=”loess”)

The plot, shown in Figure 8, shows that there is indeed a huge variation in the (small number) of datapoints, and the confidence intervals reflect that. An interesting feature is that some small remote schools have above average scores. If we were doing a project on this data, this would be a feature worth pursuing further as it would likely be of interest to education policymakers.

Note that there is a difference in the x axis scale between Figures 7 and 8 – the former goes from 0 to 2000 whereas the latter goes up to 400 only. So for a fair comparison, between remote and other areas, you may want to re-plot Figure 7, zooming in on student numbers between 0 and 400 (or even less). This will also enable you to see the complicated dependence of scores on student numbers more clearly across all regions.

We’ll leave the scores vs student numbers story there and move on to another geometry – the well-loved bar chart. The first one is a visualisation of the remoteness category count that we did earlier. The relevant geometry function is *geom_bar*, and the code is as easy as:

The plot is shown in Figure 9.

The category labels on the x axis are too long and look messy. This can be fixed by tilting them to a 45 degree angle so that they don’t run into each other as they most likely did when you ran the code on your computer. This is done by modifying the *axis.text* element of the plot theme. Additionally, it would be nice to get counts on top of each category bar. The way to do that is using another geometry function, geom_text. Here’s the code incorporating the two modifications:

theme(axis.text.x=element_text(angle=45, hjust=1))

The result is shown in Figure 10.

Some things to note: : *stat=count* tells ggplot to compute counts by category and the aesthetic *label = ..count.. *tells ggplot to access the internal variable that stores those counts. The the vertical justification setting, *vjust=-1*, tells ggplot to display the counts on top of the bars. Play around with different values of *vjust* to see how it works. The code to adjust label angles is self explanatory.

It would be nice to reorder the bars by frequency. This is easily done via *fct_infreq* function in the *forcats* package like so:

geom_bar(mapping = aes(x=fct_infreq(ASGS_remoteness)))+

theme(axis.text.x=element_text(angle=45, hjust=1))

The result is shown in Figure 11.

To reverse the order, invoke *fct_rev,* which reverses the sort order:

geom_bar(mapping = aes(x=fct_rev(fct_infreq(ASGS_remoteness))))+

theme(axis.text.x=element_text(angle=45, hjust=1))

The resulting plot is shown in Figure 12.

If this is all too grey for us, we can always add some colour. This is done using the *fill* aesthetic as follows:

geom_bar(mapping = aes(x=ASGS_remoteness, fill=ASGS_remoteness))+

theme(axis.text.x=element_text(angle=45, hjust=1))

The resulting plot is shown in Figure 13.

Note that, in the above, that we have mapped fill and x to the *same* variable, *remoteness *which makes the legend superfluous. I will leave it to you to figure out how to suppress the legend – Google is your friend.

We could also map fill to another variable, which effectively adds another dimension to the plot. Here’s how:

geom_bar(mapping = aes(x=ASGS_remoteness, fill=level_of_schooling))+

theme(axis.text.x=element_text(angle=45, hjust=1))

The plot is shown in Figure 14. The new variable, level of schooling, is displayed via proportionate coloured segments stacked up in each bar. The default stacking is one on top of the other.

Alternately, one can stack them up side by side by setting the *position *argument to *dodge* as follows:

geom_bar(mapping = aes(x=ASGS_remoteness,fill=level_of_schooling),position =”dodge”)+

theme(axis.text.x=element_text(angle=45, hjust=1))

The plot is shown in Figure 15.

Finally, setting the *position* argument to *fill* normalises the bar heights and gives us the proportions of* level of schooling* for each *remoteness* category. That sentence will make more sense when you see Figure 16 below. Here’s the code, followed by the figure:

geom_bar(mapping = aes(x=ASGS_remoteness,fill=level_of_schooling),position = “fill”)+

theme(axis.text.x=element_text(angle=45, hjust=1))

Obviously, we lose frequency information since the bar heights are normalised.

An interesting feature here is that the proportion of central and community schools increases with remoteness. Unlike primary and secondary schools, central / community schools provide education from Kindergarten through Year 12. As remote areas have smaller numbers of students, it makes sense to consolidate educational resources in institutions that provide schooling at all levels .

Finally, to close the loop so to speak, let’s revisit our very first plot in Figure 1 and try to simplify it in another way. We’ll use faceting to split it out into separate plots, one per remoteness category. First, we’ll organise the subplots horizontally using *facet_grid*:

facet_grid(~ASGS_remoteness)

The plot is shown in Figure 17 in which the different remoteness categories are presented in separate plots (facets) against a common y axis. It shows, the sharp differences between student numbers between remote and other regions.

To get a vertically laid out plot, switch the faceted variable to other side of the formula (left as an exercise for you).

If one has too many categories to fit into a single row, one can wrap the facets using *facet_wrap* like so:

geom_point(mapping = aes(x=student_number,y=ICSEA_Value))+

facet_wrap(~ASGS_remoteness, ncol= 2)

The resulting plot is shown in Figure 18.

One can specify the number of rows instead of columns. I won’t illustrate that as the change in syntax is quite obvious.

…and I think that’s a good place to stop.

### Wrapping up

Data visualisation has a reputation of being a dark art, masterable only by the visually gifted. This may have been partially true some years ago, but in this day and age it definitely isn’t. Versatile packages such as ggplot, that use a consistent syntax have made the art much more accessible to visually ungifted folks like myself. In this post I have attempted to provide a brief and (hopefully) logical introduction to ggplot. In closing I note that although some of the illustrative examples violate the principles of good data visualisation, I hope this article will serve its primary purpose which is pedagogic rather than artistic.

**Further reading**:

Where to go for more? Two of the best known references are Hadley Wickham’s books:

I highly recommend his R for Data Science , available online here. Apart from providing a good overview of ggplot, it is an excellent introduction to R for data scientists. If you haven’t read it, do yourself a favour and buy it now.

People tell me his ggplot book is an excellent book for those wanting to learn the ins and outs of ggplot . I have not read it myself, but if his other book is anything to go by, it should be pretty damn good.