Hi everybody,

Hi Dr Nick. But enough of that – today I’m going to be working through a Kaggle problem. For those of you who don’t know Kaggle, I can’t advise in favour of it strongly enough. It’s a great place to have a go at using real data sets to apply various machine learning techniques. There’s a leaderboard, discussions on methods and some non-too shabby prizes. I’ll come clean at this point – I’m not a natural salesperson.

I believe there’s something of a taboo against posting solutions/methods for Kaggle – however, I think I’m good to write about a method of solving this particular problem. The digit recognizer problem seems to be a rolling competition with a bunch of already published results and a few training classes on how to solve it. Let me know if you think this is overstepping the mark.

So, the problem:
Given a big set (42,000) of labelled training data (28 x 28 black and white images) of handwritten images (0-9) are we able to correctly identify other (identically dimensioned) handwritten digits.

There are a whole bunch of ways of doing this and the method I’ve had best success with is Support Vector Machines (using LibSVM). I may post an example of how to run that for this particular example but today I’d like to look at Adaboost (using the MultiBoost package)…

Until fairly recently I was entirely ignorant of Adaboost – I came across it on a different Kaggle problem (the Higgs one). There, a number of ‘out of the box’ methods were showcased – the most successful of which was Adaboost. A bit of reading on Adaboost suggests that it’s a fairly well-regarded, and successful method of performing a range of machine learning tasks. It’s also sometimes cited as being the best ‘out of the box’ (not specifically designed for the task at hand) algorithm in machine learning.

My current intuition on Adaboost is that it’s basically a ‘rule of thumb’ algorithm. It takes a lot of very simple decision boundaries and uses them to create a more complicated decision space. I say rule of thumb because I imagine a car mechanic or a doctor trying to diagnose a fault. The patient presents with symptom x, that makes a whole bunch of things less likely. However, if the patient falls into this age bucket and this ethnic group, some of the previously discounted things become more likely. I don’t know if that sort of explanation helps you but I quite like it. Basically, you create a simple rule that’s more often right than wrong. However, you can then update it with as many exceptions as you’ve got other bits of data. I think that’s a lot how the human decision-making process goes.

Anyway, all this talking isn’t getting us closer to a juicy set of predictions. Mad props to whoever first generated this particular procedure for the Higgs problem – I’ve shamelessly ripped it off, only making changes where necessary for this problem.


import random
import csv
import subprocess
import numpy as np

def DataToArff(dataset, labels, header, title, filename):
    With this data structure we're able to turn an arbitrary string of data into a .arff file
    These files allow us to import the data into Multiboost or Weka (amongst other machine learning libraries
    with open(filename + ".arff", 'w') as f:
        f.write('@RELATION ' + title + 'nn')
        for feature in header:
            f.write('@ATTRIBUTE ' + feature + ' NUMERICn')
        f.write('@ATTRIBUTE class {0,1,2,3,4,5,6,7,8,9}n')
        ## We could do this using all_data - however, we need the labels for further work
        ## Additionally, if the labels were numeric variables we'd be able to leave the rest of our work unchanged and handle them here
        for datarow, label in zip(dataset, labels):
            for value in datarow:
                f.write(str(value) + ',')
            f.write(str(label) + 'n')

all_data = list(csv.reader(open('train.csv', 'rb'), delimiter=','))
header = np.array(all_data[0][1:])
dataset = np.array([map(float, row[1:]) for row in all_data[1:]])
(numpoints, numfeatures) = dataset.shape

# Labels on the first column of the line
labels = np.array([row[0] for row in all_data[1:]])

randomPermutation = random.sample(range(len(dataset)), len(dataset))
## If this breaks halfway through, we'll be glad to be able to load our random permutation
np.savetxt('randomPermutation.csv', randomPermutation, fmt='%d', delimiter=',')

## I'll change the proportion of the train set and see how we get on.
numpointsTrain = int(numpoints*0.75)
numpointsValidatin = numpoints - numpointsTrain

## Because we've got a random permutation there's no problem taking slices of the total set to sort into train and validation
datasetTrain = dataset[randomPermutation[:numpointsTrain]]
datasetValidation = dataset[randomPermutation[numpointsTrain:]]

labelsTrain = labels[randomPermutation[:numpointsTrain]]
labelsValidation = labels[randomPermutation[numpointsTrain:]]

DataToArff(datasetTrain, labelsTrain, header, 'DigitsML_challenge_train', 'training')
DataToArff(datasetValidation, labelsValidation, header, 'DigitsML_challenge_validation', 'validation')

## Our Adaboost parameters are wholly contained in the relevant config files
p1 = subprocess.Popen(['/home/matt/Downloads/multiboost-1.2.02-x64-linux', '--configfile', 'config.txt'])
p2 = subprocess.Popen(['/home/matt/Downloads/multiboost-1.2.02-x64-linux', '--configfile', 'configScoresValidation.txt'])

testText = list(csv.reader(open('test.csv', 'rb'), delimiter=','))
datasetTest = np.array([map(float, row[:]) for row in testText[1:]])
labelsTest = np.repeat('0', len(testText) - 1)

DataToArff(datasetTest, labelsTest, header, 'Digit_challenge_test', 'test')

p3 = subprocess.Popen(['/home/matt/Downloads/multiboost-1.2.02-x64-linux', '--configfile', 'configScoresTest.txt'])

testScoresText = list(csv.reader(open('scoresTest.txt', 'rb'), delimiter=','))
with open('submission.csv', 'w') as f:
    for index, entry in enumerate(testScoresText):
        ## Take the index of the maximum value for a given row - this is the most likely value
        f.write(str(index + 1) + "," + str(np.argmax(entry)) + 'n')

I’d like to think that that bit of code is reasonably transparent and clear on what it’s doing. If I’m wrong, a basic explanation:
1.) Randomly split the data into a training and validation set
2.) Create .arff files for both of these sets
3.) Run Multiboost (our Adaboost implementation) on the training set and validation set
4.) Using the files created from our train/validation Adaboost, get the test set and generate predictions (again using Multiboost)
5.) Generate a submission file in the required format

Easy does it. Now for the configuration files that I’m using:


fileformat arff
verbose 2
learnertype TreeLearner
seed 50
weightpolicy balanced
baselearnertype SingleStumpLearner 8
outputinfo results.dta e01w01auc
traintest training.arff validation.arff 5000
shypname shyp.xml


posteriors validation.arff shyp.xml scoresValidation.txt 5000
fileformat arff
verbose 2
learnertype TreeLearner
baselearnertype SingleStumpLearner 8


posteriors test.arff shyp.xml scoresTest.txt 5000
fileformat arff
verbose 2
learnertype TreeLearner
baselearnertype SingleStumpLearner 8

I’ll be honest and say I’ve not really found the ideal set-up for this problem here. I’m able to get a score of around 0.965 using the ones here but if you look at the leader board you’ll see that’s not all that good. Certainly the LibSVM method performed much better (something like 0.99). Not to worry, it’s doing the right thing, generating good results and is another tool in our arsenal.

The World Cup may stymie┬ámy blog posts for a bit – then again, supporting England, it might only be 3 games.

Football’s coming home.