# Cosine Similarity, Pearson Correlation, Inner Products

## To begin, a criticism

I picked up the Haskell Data Analysis Cookbook. The book presents examples of comparing data using Pearson Coefficient and using Cosine Similarity.

pearson xs ys = (n * sxy - sx * sy) / sqrt ((n * sxx - sx * sx) * (n * syy - sy * sy))
where
n = fromIntegral (length xs)
sx = sum xs
sy = sum ys
sxx = sum $zipWith (*) xs ys syy = sum$ zipWith (*) ys ys
sxy = sum $zipWith (*) xs ys cosine xs ys = dot d1 d2 / (len d1 * len d2) where dot a b = sum$ zipWith (*) a b
len a = sqrt $dot a a  Although these code snippets are both calculating the ‘similarity’ between two vectors and actually, as we shall see, share a lot of structure, this is not at all apparent from a glance. We can fix that however… ## Definition of an Inner Product An inner product is conceptually a way to see how long a vector is after projecting it along another (inside some space). Formally, an inner product is a binary operator satisfying the following properties ###### Linearity $\langle u+v,w \rangle = \langle u,w\rangle + \langle v,w\rangle$ $\langle \alpha u,w\rangle = \alpha\langle u,w\rangle$ for $\alpha \in \mathbb{R}$ We are saying that we can push sums inside on the left to being outside. We can also push out constant factors. ###### (Conjugate) Symmetry $\langle u,v \rangle = \langle v,u \rangle$ or in the complex case, $\langle u,v \rangle = \overline{\langle v,u \rangle}$ In the real case, we’re saying everything is symmetric – it doesn’t matter which way you do it. In the complex case we have to reflect things by taking the conjugate. ###### Positive Definiteness $\langle u,u \rangle \ge 0$ with equality iff $u = 0$ Here we’re saying projecting a vector onto itself always results in a positive length. Secondly, the only way we can end up with a result of zero is if the vector itself is of length 0. ### From Inner Product to a notion of ‘length’ Intuitively a distance between two things must be • positive or zero (a negative distance makes not too much sense), with a length of zero corresponding to the zero vector • linear (if we scale the vector threefold, the length should also increase threefold) Given that $\langle u,u \rangle \ge 0$ we might be tempted to set $length(a) := \langle u,u \rangle$ but then upon scaling $u \rightarrow \alpha u$ we get $length(\alpha u) := \langle \alpha u, \alpha u \rangle = \alpha^2 \langle u,u \rangle$ – we’re not scaling linearly. Instead defining $||a|| := \sqrt{\langle a,b \rangle}$ everything is good! ### Similarity Now, in the abstract, how similar are two vectors? How about we first stop caring about how long they are, and want them just to point in the same direction. We can project one along the other and see how much it changes in length (shrinks). Projecting is kind of like seeing what its component is in that direction – i.e. considering 2-dimensional vectors in the plane, projecting a vector onto a unit vector in the $x$ direction will tell you the $x$ component of that vector. Let’s call two vectors $a$ and $b$. Firstly let’s scale them to be both of unit length, $\hat{a} = \frac{a}{||a||}, \hat{b} = \frac{b}{||b||}$ Now, project one onto the other (remember we’re not caring about order because of symmetry). $similarity(a,b) := \langle \frac{a}{||a||}, \frac{b}{||b||} \rangle$ Using linearity we can pull some stuff out (and also assuming everything’s happily a real vector – not caring about taking conjugates)… $similarity(a,b) := \frac{\langle a, b \rangle}{||a|| ||b||}$ ## Making Everything Concrete ###### Euclidean Inner Product The dot product we know and love. $a \dot b = a_1 b_1 + \dots + a_n b_n$ Plugging that into the similarity formula, we end up with the cosine similarity we started with! ###### Covariance Inner Product The covariance between two vectors is defined as $Cov(X,Y) = \mathbb{E}((X - \mathbb{E}(X))(Y - \mathbb{E}(Y)))$ where we’re abusing the notion of expectation somewhat. This in fact works if X and Y are arbitrary L2 random variables… but for the very concrete case of finite vectors we could consider $\mathbb{E}(X) = \frac{1}{n}(x_1 + \dots + x_n)$. We’ve said in our space, to project a first vector onto a second we see how covariant the first is with the second – if they move together or not. Plugging this inner product into the similarity formula, we instead get the pearson coefficient! In fact, given $Cov(X,X) = Variance(X)$, in this space we have $length(X) = \sqrt{Variance(X)} = StdDev(X) =: \sigma_X$, i.e. $similarity(X,Y) = \frac{Cov(X,Y)}{\sigma_X \sigma_Y}$. ### Improving the code Now that we know this structure exists, I posit the following as being better similarity ip xs ys = (ip xs ys) / ( (len xs) * (len ys) ) where len xs = sqrt(ip xs xs) -- the inner products dot xs ys = sum$ zipWith (*) xs ys

covariance xs ys = exy - (ex * ey)
where e xs = sum xs / (fromIntegral $length xs) exy = e$ zipWith (*) xs ys
ex = e xs
ey = e ys

-- the similarity functions
cosineSimilarity = similarity dot
pearsonSimilarity = similarity covariance


## Things I’m yet to think about

…though maybe the answers are apparent.

We have a whole load of inner products available to us. What does it mean to use those inner products?
E.g. $\langle f,g \rangle = \int^{-\pi}_{\pi} f(t) \overline{g(t)} \, \mathrm{d}t$ on $\mathbb{L}^2[-\pi,\pi]$ – the inner product producing the Fourier transform. I’m not the resulting similarity is anything particularly special though…

# Notes: First Steps with the Interactive Brokers API (OS X Mavericks)

## Setup

### IB Workstation

In order to use the API the IB Workstation must be installed and running – one then connects to this using the API. Installation is available for Mac, Linux and Windows – for mac the automatic pkg is here.

### IB API

Next step is to download the API – a collection of java source files which handle talking to the IB Workstation. Download the jar and extract it somewhere.

### IB Controller

Manually running the GUI app each time/manually clicking through dialogs etc, is a pain. To mitigate this the IB Controller github project provides a wrapper around launching/controlling the app. Getting this working was just a matter of following the userguide.

## Creating a Simple Automated Client (Eclipse)

Ultimately this’ll be done with gradle, but for now let’s just quickly use eclipse.

• Create a new eclipse project
• Add the folder ‘javaclient’ from wherever the API was extracted as a linked source folder (right click on project -> build path -> link source folder)
• We’ll use classes in com.ib.controller
• instantiate an ApiController
• instantiate a NewContract
• instantiate a NewOrder
• call the controller’s placeOrModifyOrder method (for now just use a no-op IOrderHandler)

## Next Steps

I feel it’s important for this type of thing (i.e. time sensitive, immediate monetary penalties for errors) to focus on verification/programming defensively.

Thinking in pseudocode I feel like I want to place an order, and be notified if it’s not filled in \$timeout seconds/any other error happens. I also feel like I want to maintain a local version of the truth, and always compare this against what the API reports as the truth before doing anything.

# List of Datasets, List of Lists of Datasets

Consider this sort of a public-facing list of datasets I’ve found interesting, have played with or want to play with.

# List of Datasets

### Lending Club

Peer to peer credit marketplace Lending Club publishes data on issued and declined loans. https://www.lendingclub.com/info/download-data.action

### World Health Organisation

The WHO publishes many interesting datasets at http://www.who.int/research/en/. They don’t however do a great job of linking to the raw datasets: http://www.who.int/healthinfo/statistics/mortality_rawdata/en/ is a comprehensive dataset providing mortality rates for all reporting countries, but difficult to find from the navigation.

### New York Times

The New York Times has a fairly comprehensive open api, documented at http://developer.nytimes.com/docs

### Divvybikes

The Chicago public cycle hire scheme (akin to New York’s Citibike, London’s Barclays Boris Bike) published data on 750 000 trips made for their data challenge. http://divvybikes.com/datachallenge

### Outpan

Outpan aims to provide a single database for turning barcodes into product information. Not extremely complete. http://www.outpan.com/index.php

### Medicare

Under the efforts of transparency, a dataset containing information around usage of Medicare. Could make a complement to some of the other medical datasets available. http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Physician-and-Other-Supplier.html

# List of Lists of Datasets

Using the spark shell, one can define classes on-the-fly and then use these classes in your distributed computation.

###### Contrived Example
scala> class Vector2D(val x: Double, val y: Double) extends Serializable {
| def length = Math.sqrt(x*x + y*y)
| }
defined class Vector2D
scala> val sourceRDD = sc.parallelize(Seq((3,4), (5,12), (8,15), (7,24)))
sourceRDD: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[5] at parallelize at :13
scala> sourceRDD.map(x => new Vector2D(x._1, x._2)).map(_.length).collect()
14/03/30 09:21:59 INFO SparkContext: Starting job: collect at :17
...snip...
res1: Array[Double] = Array(5.0, 13.0, 17.0, 25.0)


In order for the remote executors here to actually run your code, they must have knowledge of the Vector2D class, yet they’re running on a different JVM (and probably different physical machine). How do they get it?

• we choose a directory on disk to store the class files
• a virtual directory is created at SparkIMain:101
• a scala compiler is instantiated with this directory as the output directory at SparkIMain:299
• this means that whenever a class is defined in the REPL, the class file is written to disk
• a http server is created to serve the contents of this directory at SparkIMain:102
• we can see info about the Http server in the logs:
14/03/23 23:39:21 INFO HttpFileServer: HTTP File server directory is /var/folders/8t/bc2vylk13j14j13cccpv9r6r0000gn/T/spark-1c7fbed7-5c87-4c2c-89e8-be95c2c7ac54
14/03/23 23:39:21 INFO Executor: Using REPL class URI: http://192.168.1.83:61626
• the http server url is stored in the Spark Config, which is shipped out to the executors
• the executors install a URL Classloader, pointing at the Http Class Server at Executor:74

For the curious, we can figure out what the url of a particular class is and then go check it out in a browser/with curl.

def urlOf[T:ClassTag] = {
val clazz = implicitly[ClassTag[T]].erasure
Seq(sc.getConf.get("spark.repl.class.uri"),
clazz.getPackage.getName,
clazz.getName).mkString("/")
}


### Do it yourself

It’s pretty trivial to replicate this ourselves – in Spark’s case we have a scala compiler which writes the files to disk, but assuming we want to serve classes from a fairly normal JVM with a fairly standard classloader, we don’t even need to bother with the to disk. We can grab the class file using getResourceAsStream. It also doesn’t require any magic of scala – an example class server in java using Jetty:

class ClasspathClassServer {
private Server server = null;
private int port = -1;

void start() throws Exception {
System.out.println("Starting server...");
if(server != null) {
return;
}

server = new Server();
NetworkTrafficSelectChannelConnector connector = new NetworkTrafficSelectChannelConnector(server);
connector.setPort(0);

ClasspathResourceHandler classpath = new ClasspathResourceHandler();

server.setHandler(classpath);
server.start();

port = connector.getLocalPort();
System.out.println("Running on port " + port);
}

class ClasspathResourceHandler extends AbstractHandler {
@Override
public void handle(String target, Request baseRequest, HttpServletRequest request, HttpServletResponse response)
throws IOException, ServletException {
System.out.println("Serving target: " + target);

try {
Class<?> clazz = Class.forName(target.substring(1));
InputStream classStream = clazz.getResourceAsStream('/' + clazz.getName().replace('.', '/') + ".class");

response.setContentType("text/html;charset=utf-8");
response.setStatus(HttpServletResponse.SC_OK);
baseRequest.setHandled(true);

OutputStream os = response.getOutputStream();

IOUtils.copy(classStream, os);
} catch(Exception e) {
System.out.println("Exception: " + e.getMessage());
baseRequest.setHandled(false);
}
}
}
}


It’s then just a matter of setting up a URL Classloader on the other side!

### Further Examples

http://quoiquilensoit.blogspot.com/2008/02/generic-compute-server-in-scala-with.html: An example of using a similar technique to write a ‘compute server’ in scala – somewhat akin to a very stripped down version of Spark.

# Chunkify – Grouping series in pandas for easy display

Continuing with the same population data-set from last time, say we wanted to group countries by when their last valid entry in the population data table.

last_year_of_data = pop_stats.groupby('Name')['Year'].max().reset_index()

Name Year
0 Albania 2004
1 Antigua and Barbuda 1983
2 Argentina 1996
3 Armenia 2012
4 Australia 2011

Let’s say we want to display these groups of countries in chunks – enter chunkify.

def chunkify(series, into=4):
#ensure the series has a sequential index
series = series.reset_index(drop=True)
#chunk the series into columns
columns = [series[range(i, series.size, into)]
.dropna()
.reset_index(drop=True)
for i in range(into)]
#stick the columns together
df = pd.concat(columns, axis=1)
#rename the columns sequentially
df.columns = range(into)
return df


Usage is simple.

last_year_of_data.groupby('Year')['Name'].apply(lambda x : chunkify(x, 3))\
.fillna('')


Voilà!

(truncated…)

0 1 2
Year
2008 0 Malaysia Maldives
2009 0 Fiji Iceland Ireland
1 Jordan New Zealand South Africa
2010 0 Bahrain Belarus France
1 Georgia Italy Kazakhstan
2 Kyrgyzstan Lithuania Mongolia
3 Romania Russian Federation Slovakia
4 Slovenia Sweden Switzerland
5 TFYR Macedonia United Kingdom United Kingdom, Northern Ireland
2011 0 Australia Bosnia and Herzegovina Brunei Darussalam
1 Bulgaria Cyprus Denmark
2 Egypt Finland Greece
3 Hong Kong SAR Japan Kuwait
4 Malta Mauritius Netherlands
5 Poland Portugal Qatar
6 Republic of Korea Rodrigues Singapore
7 Spain United Kingdom, Scotland
2012 0 Armenia Austria Belgium
1 Croatia Czech Republic Estonia
2 Germany Hungary Israel
3 Latvia Luxembourg Norway
4 Republic of Moldova Serbia Seychelles
5 Ukraine United Kingdom, England and Wales

# Labelled lines – prettier charts in matplotlib

The default legend for matplotlib line charts can leave a little to be desired. With many colours it can also sometimes be a little tricky to match the legend to the appropriate line. Suppose instead we place the labels next to the lines.

Smoothed Annual Population Change – WHO Population Data

Additionally here, we’ve removed the top and right axes, increased the font sizes of the labels and set the ticks to extend outwards. We firstly take our pandas dataframe death_rates_data_frame and plot it as normal (disabling gridlines and making the lines slightly thicker). We add the larger axis labels.

population_data_frame.plot(legend=False, grid=False, linewidth=2, figsize=(9,6))
plt.xlabel('Year', fontsize=20)
plt.ylabel('Population \n% Change', rotation='horizontal', labelpad=80, fontsize=20)


Next we define a function that, given a set of axes, will

• Hide the top and right axes
• Set the ticks to only display on the bottom x-axis and the left y-axis
• Set the ticks to extend outwards
• Increase the font-size of the labels
def format_graph(ax):
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.tick_params(axis='both', direction='out', labelsize=14)


The label_lines function handles the placing of text next to the final vertex of the line.

def label_lines(ax, offset, label_formatter=None, **kwargs):
for handle, label in zip(*ax.get_legend_handles_labels()):
path = handle.get_path()
#careful with the NaNs
last_vertex = pd.DataFrame(path.vertices).dropna().as_matrix()[-1]
nicer_label = label_formatter(label) if label_formatter else label
plt.text(last_vertex[0]+offset[0], last_vertex[1]+offset[1], nicer_label, color=handle.get_color(), transform=ax.transData, **kwargs)


Finally we call the above code with the current axes! Given the slight label overlap, let’s outline the text in white with path_effects.

import matplotlib.patheffects as PathEffects
ax = plt.gca()
format_graph(ax)
label_lines(ax, offset=(1,0),
fontsize=16,
path_effects=[PathEffects.withStroke(linewidth=3, foreground="w")])


Voilà.

# Connecting to Vertica from Spark

So you have a lot of data in vertica, and you want to do analytics beyond what’s easily expressible in vSQL, at scale, without writing nasty C++ UDFs; or perhaps you already have a lot of data already sitting in HDFS to join against.

Enter spark.

1. Grab the vertica jbdc drivers and hadoop connectors from the vertica support portal and put them on your spark classpath (e.g. via ADD_JARS)

2. Use something like this class

import org.apache.spark.rdd.RDD
class Vertica(val hostnames: String,
val database: String,
val port: String = "5433") extends Serializable {

def configuration:Configuration = {
val conf = new Configuration
conf.set("mapred.vertica.hostnames", hostnames)
conf.set("mapred.vertica.database", database)
conf.set("mapred.vertica.port", port)
conf
}

def query(sql: String):RDD[VerticaRecord] = {
val job = new Job(configuration)
job.setInputFormatClass(classOf[VerticaInputFormat])

VerticaInputFormat.setInput(job, sql)

val vertica = new Vertica("my-node-1,my-node-2,my-node-3", "my-db", "username", "password")