Creating a price model using k-Nearest Neighbours + Genetic Algorithm

Chapter 8 of Programming Collective Intelligence (PCI) explains the usage and implementation of the k-Nearest Neighbours algorithm. (k-NN).

Simply put:
k-NN is a classification algorithm that uses (k) for the number of neighbours to determine what class an item will belong to.  To determine the neighbours to be used the algorithm uses a distance / similarity score function, in this example (Euclidian Distance).

PCI takes it a little further to help with accuracy in some scenarios. This includes the usage of a weighted average of the neighbours, as well as then using either simulated annealing or genetic algorithms to determine the best weights, building on Optimization techniques - Simulated Annealing & Genetic Algorithms

As with all the previous chapters the code is in my github repository.

So the similarity score function looked like (slightly different to the one used earlier, which was inverted to return 1 if equals):

The simulated annealing and genetic algorithm code I updated as I originally implemented them using Ints... (lesson learnt when doing anything it do with ML or AI, stick to doubles).

Then finally putting it all together my Java implementation of the PCI example

While reading up some more on k-NN I also stumbled upon the following blog posts
First one describing some of the difficulties around using k-NN.
k-Nearest Neighbors - dangerously simple

And then one giving a great overview of k-NN
A detailed introduction to k-NN algorithm

Decision Trees

I just completed working through Chapter 7 of Programming Collective Intelligence (PCI). This chapter demonstrates how, when and who you should use the decision tree construct. The method described was the CART technique.

The basic summary is: A decision tree has each branch node represent a choice between a number of alternatives, and each leaf node represents a decision or (classification). This makes decision tree another supervised machine learning algorithm useful in classifying information.

The main problem it overcome in defining a decision tree is how to identify the best split of the data points. To find this you need to go through all the sets of data, and identify which will give you the best split (gain) and start from there.
For some more technical information about this split / gain:
http://en.wikipedia.org/wiki/Information_gain_in_decision_trees

The biggest advantages I see in using a decision tree are:
It's easy it is to interpret and visualise.
Data didn't need to be normalised or something between -1 and 1.

Decision trees however cant be effectively used on large datasets with a large number of results.

As with my previous Classifiers post, I ended up using SQLite in memory db as it's such a pleasure to use. I did venture into using LambdaJ, but it actually ended up being such an ugly line of code I left it and simply did it manually. I have not looked at the Java 8 implementation of lambdas yet, I just hope it doesn't end in code like (with a whole bunch of static imports):

falseList.add(filter(not(having(on(List.class).get(col).toString(), equalTo((String) value))), asList(rows)));

So my java implementation of the PCI decision tree ended up looking like (All code in Github) :

(once again ...  about 50% more code :) ).. really beginning to enjoy Python, I do see me using that for all future AI / ML type work as a first choice.

Document Filtering - Classifiers

Chapter 6 of Programming Collective Intelligence (PCI) demonstrates how to classify documents based on their content.

I used one extra Java open source library for this chapter, and it's implementation was completely painless.

Sqlite

What a pleasure, simple maven include, and thats it's little file or memory based SQL based db in your code.

My full java implementation of some of the topics are available on my GitHub repo, but will highlight the Fisher Method (or  Fisher's discriminant analysis or LDA) if you want to get a lot more technical.

What has made PCI a good book is it's ability to summarise quite complex theoretical and mathematical concepts down to basics and code, for us lowly developers use to practically.

To Quote:

"the Fisher method calculates the probability of a category for each feature of the document, then combines the probabilities and test to see if the set of probabilities is more or less likely than a random set. This method also returns a probability for each category that can be compared to others"

Although for this particular chapter and example, I would have liked a bit more layman's information surrounding Chi-squared distribution, as you will notice in the TODO of the following code example.
During the writing of this post, I discovered the following blog:
Shape of data
Seems well worth the read, will be spending the next couple days on that before continuing with PCI, chapter 7.. Decision Trees.

Optimization techniques - Simulated Annealing & Genetic Algorithms

Chapter 5 of Programming Collective Intelligence (PCI) deals with optimisation problems.

To Quote:
"Optimisation finds the best solution to a problem by trying many different solution and scoring them to determine their quality. Optimisation is typically used in cases where there are too many possible solutions to try them all"

Before embarking on this chapter I decided that it would be best to quickly learn Python, there just seems to be a lot of Python around as soon as you start learning and reading about machine learning and data analysis, it can't actually be ignored.
(Still not sure why this is the case, but set out to get up an running with Python, in 1 weekend)

Some of the resources I used:
http://www.python.org
http://www.stavros.io/tutorials/python/
http://www.diveintopython.net/index.html
http://docs.python-guide.org/en/latest/

As a developer, learning the basics of Python really isn't very difficult,  to be honest it probably took me longer to find an development environment I was happy with, consoles and text editors just don't do it for me.

The main ones I investigated were:
Ninja IDE (Free)
Eclipse + PyDev (Free)
PyCharm ($99)

I spent quite a bit of time playing with Ninja IDE and Eclipse, but there were just little things that kept bugging me, from strange short cuts to highlighting correct code / syntax as incorrect.

10 minutes after installing PyCharm, I was sold. To be fair, I am probably not the best person to judge.
I code in IntelliJ daily and actually ended up converting all the java developers in my department to drop Eclipse and start using IntelliJ.... I also did the majority of my Objective-C work in AppCode, in other words... I am a JetBrains fanboy, happy to hand over my money for an awesome tool.

Getting back to PCI, where were a couple issues with the code in this chapter, which caused me (a person that just learnt Python) a little bit of pain, 'cause I figured the code had to be right and I was just doing something wrong, eventually I went searching and found:

PCI Errata

With that I corrected the issues in the hillclimb and genetic algorithm functions:

The java implementation for the 3 functions ended up twice as long and looking like:

And unlike my previous posts on PCI, I didn't use a whole bunch of open source libraries, only added one.
Java Tuples.

The whole Chapter 5 Optimisation solution is in my Blog Github repo, the concepts used in both the Simulated Annealing and Genetic Algorithm could easily be adapted and used again if looking for a simple example of those concepts.

Now for Chapter 6 ... Document Filtering...

Some Java based AI Frameworks : Encog, JavaML, Weka

While working through I am working through Programming Collection Intelligence I found myself sending a lot of time translating the Python code to java, being typically impatient at my slow progress, I went searching for alternatives.

I found 3:
Encog - Heaton Research
JavaML
Weka

This is by no means an in-depth investigation, I simply downloaded what the relevant projects had available and quickly compared what was available to me to learn and implement AI related samples / applications.
 

Encog

Advantages

1. You Tube video tutorials
2. E-Books available for both Java and .Net
3. C# implementation
4. Closure wrapper
5. Seems active

Disadvantages

1. Quite large code base to wrap your head around, this is probably due to the size of the domain we are looking at, but still much more intimidating to start off with vs. the Java ML library.

JavaML

Advantages

1. Seems reasonably stable
2. Well documented source code
3. Well defined simple algorithm implementations

Disadvantages

1. Lacks the tutorial support for a AI newbie like myself

Weka

Advantages

1. E-Book Data Mining Practical Techniques

Disadvantages

1. Could not install Weka 3-7-9 dmg... kept on giving me a "is damaged and can't be opened error, so left it there, as Sweet Brown says: "Ain't nobody got time for that". 

So no surprise I went with Encog, and started on their video tutorials....
A couple hours later, first JUnit test understanding, training and testing a Hopfield neural network using the Encog libs.

Similarity Score Algorithms

As per my previous post, I am working through Programming Collection Intelligence the first couple algorithms described in this book are regarding finding a similarity score, the methods they work through are Euclidean Distance and the Pearson Correlation Coefficient. The Manhattan distance score is also mentioned but some what I could find it seems that it is just the sum of the (absolute) differences of their coordinates, instead of Math.pow 2 used in Euclidean distance.

I worked through this and wrote/found some java equivalents for future use:

Euclidean Distance:

Pearson Correlation Coefficient: