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Machine Learning Tool Kit

We will always use ARFF files for our datasets, and we will make the assumption that all data will fit in RAM. Details on ARFF are found here. A collection of data sets already in the ARFF format can be found here.

A basic tool kit is provided in Java and C++ to help you get started implementing learning algorithms. In order to facilitate mentoring, I strongly recommend implementing the projects in Eclipse (see video tutorial below). You are also welcome to code up your own toolkit or modify the source code made available here however you want. If you do so, here are a few things to keep in mind:

The toolkit is intended as a starting place for working with machine learning algorithms. It provides the following functionality to run your algorithms:

Build Instructions for the Java version

  1. Download the zip file here.
  2. Unzip the zip file
  3. javac *.java
(Though we have not tested it, this should work in Microsoft Visual C++. You will need to create a project solution. If you use Windows and have trouble, come see us for help.)

Build Instructions for the C++ version

  1. Open the terminal
  2. wget
  3. unzip
  4. cd toolkit/src/
  5. make opt

Toolkit Validation Instructions

  1. mkdir datasets
  2. cd datasets/
  3. wget
  4. cd ..
  5. ./bin/MLSystemManager -L baseline -A datasets/iris.arff -E training
You should see the results for a baseline classifier (33% accuracy on iris)

Alternate Toolkits

Students from previous semesters have ported the toolkit to various other languages. We list below links to the Github repositories for two of the more popular alternative languages, Python and C#. Please note that while previous students have successfully used both of these languages for the course, these ported versions are more likely to contain uncaught bugs. The professor will be in a better place to help you if you use the Java or C++ versions of the toolkit.



Video Tutorial for Java toolkit installation

Download the toolkit here.

Access the Iris dataset here.

Usage Instructions

MLSystemManager -L [LearningAlgorithm] -A [ARFF_File] -E [EvaluationMethod] {[ExtraParameters]} [-N] [-R seed]

Where the -N will normalize the training and test data sets (Normalization max and min will come from the training set).

The -R allows you pass in a seed for the random number generator. By default each time you run the code, the data set will be shuffled differently. If you wish to produce the same shuffle, provide a seed such as 1 or 2.

Possible evaluation methods are:

Here is an example with output:

./MLSystemManager -L dummy -A ../Research/dataSets/iris.arff -E training -N
Dataset name: iris
Dataset is normalized.
Number of instances: 150
Learning algorithm: dummy
Evaluation method: training

Accuracy on the training set:
Output classes accuracy:
Iris-setosa: 1
Iris-versicolor: 0
Iris-virginica: 0
Set accuracy: 0.333333

Accuracy on the test set:
Output classes accuracy:
Iris-setosa: 1
Iris-versicolor: 0
Iris-virginica: 0
Set accuracy: 0.333333

Time to train: 5.96046e-06 seconds (Note: If the simulation starts before midnight and ends after, the time will not be accurate)

A DummyLearner class is provided that classifies all instances as the majority class (BaselineLearner). This class can be used as a template for creating your own learning algorithms. The instances are stored in a vector of vectors of doubles (C++ version) or an ArrayList of ArrayLists of doubles (Java version). When creating a new learning algorithm, you need to add the include line in the MLSystemManager file and it must inherit from the Learner class.


Thanks to Dr. Tony Martinez for the written tutorial and toolkit development. Thanks to Mike Brodie for the video tutorial.