# Non-diffusive atmospheric flow #6: principal components analysis

The pre-processing that we’ve done hasn’t really got us anywhere in terms of the main analysis we want to do–it’s just organised the data a little and removed the main source of variability (the seasonal cycle) that we’re not interested in. Although we’ve subsetted the original geopotential height data both spatially and temporally, there is still a lot of data: 66 years of 181-day winters, each day of which has $72 \times 15$ $Z_{500}$ values. This is a very common situation to find yourself in if you’re dealing with climate, meteorological, oceanographic or remote sensing data. One approach to this glut of data is something called dimensionality reduction, a term that refers to a range of techniques for extracting “interesting” or “important” patterns from data so that we can then talk about the data in terms of how strong these patterns are instead of what data values we have at each point in space and time.

I’ve put the words “interesting” and “important” in quotes here because what’s interesting or important is up to us to define, and determines the dimensionality reduction method we use. Here, we’re going to side-step the question of determining what’s interesting or important by using the de facto default dimensionality reduction method, principal components analysis (PCA). We’ll take a look in detail at what kind of “interesting” and “important” PCA give us a little later.

PCA is, in principle, quite a simple method, but it causes many people endless problems. There are some very good reasons for this:

• PCA is in some sense nothing more than a generic change of basis operation (with the basis we change to chosen in a special way). The result of this is that a lot of the terminology used about PCA is also very generic, and hence very confusing (words like “basis”, “component”, “eigenvector”, “projection” and so on could mean more or less anything in this context!).

• PCA is used in nearly every field where multivariate data is analysed, and is the archetypical “unsupervised learning” method. This means that it has been invented, reinvented, discovered and rediscovered many times, under many different names. Some other names for it are: empirical orthogonal function (EOF) analysis, the Karhunen-Loève decomposition, proper orthogonal decomposition (POD), and there are many others. Each of these different fields also uses different terms for the different outputs from PCA. This is very confusing: some people talk about principal components, some about empirical orthogonal functions and principal component time series, some about basis functions, and so on. Here, we’re going to try to be very clear and careful about the names that we use for things to try to alleviate some of the confusion.

• There is a bit of a conceptual leap that’s necessary to go from very basic examples of using PCA to using PCA to analyse the kind of spatio-temporal data we have here. I used to say something like: “Well, there’s a nice two-dimensional example, and it works just the same in 100 dimensions, so let’s just apply it to our atmospheric data!” A perfectly reasonable reponse to that is: “WHAT?! Are you an idiot?”. Here, we’re going to take that conceptual leap slowly, and describe exactly how the “change of basis” view of PCA works for spatio-temporal data.

• There are some aspects of the scaling of the different outputs from PCA that are really confusing. In simple terms, PCA breaks your data down into two parts, and you could choose to put the units of your data on either one of those parts, normalising the other part. Which one you put the units on isn’t always an obvious choice and it’s really easy to screw things up if you do it wrong. We’ll look at this carefully here.

So, there’s quite a bit to cover in the next couple of articles. In this article, we will: explain the basic idea of PCA with a very simple (two-dimensional!) example; give a recipe for how to perform PCA on a data set; talk about why PCA works from an algebraic standpoint; talk about how to do these calculations in Haskell. Then in the next article, we will: describe exactly how we do PCA on spatio-temporal data; demonstrate how to perform PCA on the $Z_{500}$ anomaly data; show how to visualise the $Z_{500}$ PCA results and save them for later use. What we will end up with from this stage of our analysis is a set of “important” spatial patterns (we’ll see what “important” means for PCA) and time series of how strong each of those spatial patterns is at a particular point in time. The clever thing about this decomposition is that we can restrict our attention to the few most “important” patterns and discard all the rest of the variability in the data. That makes the subsequent exploration of the data much simpler.

## The basic idea of PCA

We’re going to take our first look at PCA using a very simple example. It might not be immediately obvious how the technique we’re going to develop here will be applicable to the spatio-temporal $Z_{500}$ data we really want to analyse, but we’ll get to that a little later, after we’ve seen how PCA works in this simple example and we’ve done a little algebra to get a clearer understanding of just why the “recipe” we’re going to use works the way that it does.

Suppose we go to the seaside and measure the shells of musselsWell, not really, since I live in the mountains of Austria and there aren’t too many mussels around here, so I’ll generate some synthetic data!. We’ll measure the length and width of each shell and record the data for each mussel as a two-dimensional (length, width) vector. There will be variation in the sizes and shapes of the mussels, some longer, some shorter, some fatter, some skinnier. We might end up with data that looks something like what’s shown below, where there’s a spread of length in the shells around a mean of about 5 cm, a spread in the width of shells around a mean of about 3 cm, and there’s a clear correlation between shell length and width (see Figure 1 below). Just from eyeballing this picture, it seems apparent that maybe measuring shell length and width might not be the best way to represent this data–it looks as though it could be better to think of some combination of length and width as measuring the overall “size” of a mussel, and some other combination of length and width as measuring the “fatness” or “skinniness” of a mussel. We’ll see how a principal components analysis of this data extracts these two combinations in a clear way.

The code for this post is available in a Gist. The Gist contains a Cabal file as well as the Haskell source, to make it easy to build. Just do something like this to build and run the code in a sandbox:

git clone https://gist.github.com/d39bf143ffc482ea3700.git pca-2d
cd pca-2d
cabal sandbox init
cabal install
./.cabal-sandbox/bin/pca-2d

Just for a slight change, I’m going to produce all the plots in this section using Haskell, specifically using the Chart library. We’ll use the hmatrix library for linear algebra, so the imports we end up needing are:

import Control.Monad
import Numeric.LinearAlgebra.HMatrix
import Graphics.Rendering.Chart.Easy hiding (Matrix, Vector, (|>), scale)
import Graphics.Rendering.Chart.Backend.Cairo


There are some name overlaps between the monadic plot interface provided by the Graphics.Rendering.Chart.Easy module and hmatrix, so we just hide the overlapping ones.

We generate 500 synthetic data points:

-- Number of test data points.
n :: Int
n = 500

-- Mean, standard deviation and correlation for two dimensions of test
-- data.
meanx, meany, sdx, sdy, rho :: Double
meanx = 5.0 ; meany = 3.0 ; sdx = 1.2 ; sdy = 0.6 ; rho = 0.75

-- Generate test data.
generateTestData :: Matrix Double
generateTestData =
let seed = 1023
mean = 2 |> [ meanx, meany ]
cov = matrix 2 [ sdx^2       , rho*sdx*sdy
, rho*sdx*sdy , sdy^2       ]
samples = gaussianSample seed n mean cov
in fromRows $filter ((> 0) . minElement)$ toRows samples


The mussel shell length and width values are generated from a two-dimensional Gaussian distribution, where we specify mean and standard deviation for both shell length and width, and the correlation between the length and width (as the usual Pearson correlation coefficient). Given this information, we can generate samples from the Gaussian distribution using hmatrix‘s gaussianSample function. (If we didn’t have this function, we would calculate the Cholesky decomposition of the covariance matrix we wanted, generate samples from a pair of standard one-dimensional Gaussian distributions and multiple two-dimensional vectors of these samples by one of the Cholesky factors of the covariance matrix– this is just what the gaussianSample function does for us.) We do a little filtering in generateTestData to make sure that we don’t generate any negative valuesObviously, a Gaussian distribution is not right for quantities like lengths that are known to be positive, but here we’re just generating some data for illustrative purposes, so we don’t care all that much. If we were trying to model this kind of data though, we’d have to be more careful..

The main program that drives the generation of the plots we’ll look at below is:

main :: IO ()
main = do
let dat = generateTestData
(varexp, evecs, projs) = pca dat
(mean, cov) = meanCov dat
cdat = fromRows $map (subtract mean)$ toRows dat
forM_ [(PNG, "png"), (PDF, "pdf"), (SVG, "svg")] $\(ptype, suffix) -> do doPlot ptype suffix dat evecs projs 0 doPlot ptype suffix cdat evecs projs 1 doPlot ptype suffix cdat evecs projs 2 doPlot ptype suffix cdat evecs projs 3 putStrLn$ "FRACTIONAL VARIANCE EXPLAINED: " ++ show varexp


and you can see the doPlot function that generates the individual plots in the Gist. I won’t say a great deal about the plotting code, except to observe that the new monadic API to the Chart library makes generating this kind of simple plot in Haskell no harder than it would be using Gnuplot or something similar. The plot code produces one of four plots depending on an integer parameter, which ranges from zero (the first plot above) to three. Because we’re using the Cairo backend to the Chart library, we can generate image output in any of the formats that Cairo supports–here we generate PDF (to insert into LaTeX documents), SVG (to insert into web pages) and PNG (for a quick look while we’re playing with the code).

The main program above is pretty simple: generate test data, do the PCA calculation (by calling the pca function, which we’ll look at in detail in a minute), do a little bit of data transformation to help with plotting, then call the doPlot function for each of the plots we want. Here are the plots we produce, which we’ll refer to below as we work through the PCA calculation:

Synthetic mussel shell test data for two-dimensional PCA example. Centred synthetic mussel shell test data for two-dimensional PCA example. PCA eigenvectors for two-dimensional PCA example. Data projection onto PCA eigenvectors for two-dimensional PCA example. Let’s now run through the “recipe” for performing PCA, looking at the figures above in parallel with the code for the pca function:

pca :: Matrix Double -> (Vector Double, Matrix Double, Matrix Double)
pca xs = (varexp, evecs, projs)
where (mean, cov) = meanCov xs
(_, evals, evecCols) = svd cov
evecs = fromRows $map evecSigns$ toColumns evecCols