ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp

This class implements the GRT Decision Tree classifier. Decision Trees are conceptually simple classifiers that work well on even complex classification tasks. Decision Trees partition the feature space into a set of rectangular regions, classifying a new datum by finding which region it belongs to.GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

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Remarks

This implementation is based on Ross Quinlan's ID3 Decision Tree algorithm: http://en.wikipedia.org/wiki/ID3_algorithm

/*
 GRT DecisionTree

 This examples demonstrates how to initialize, train, and use the DecisionTree algorithm for classification.
 
 Decision Trees are conceptually simple classifiers that work well on even complex classification tasks. 
 Decision Trees partition the feature space into a set of rectangular regions, classifying a new datum by 
 finding which region it belongs to.
 
 In this example we create an instance of a DecisionTree algorithm and then train a model using some pre-recorded training data.
 The trained DecisionTree model is then used to predict the class label of some test data.
 
 This example shows you how to:
 - Create and initialize the DecisionTree algorithm
 - Load some ClassificationData from a file and partition the training data into a training dataset and a test dataset
 - Train a DecisionTree model using the training dataset
 - Test the DecisionTree model using the test dataset
 - Manually compute the accuracy of the classifier

You should run this example with one argument pointing to the data you want to load. A good dataset to run this example is acc-orientation.grt, which can be found in the GRT data folder.
*/

//You might need to set the specific path of the GRT header relative to your project
#include <GRT/GRT.h>
using namespace GRT;
using namespace std;

int main(int argc, const char * argv[])
{
 //Parse the data filename from the argument list
 if( argc != 2 ){
 cout << "Error: failed to parse data filename from command line. You should run this example with one argument pointing to the data filename!\n";
 return EXIT_FAILURE;
 }
 const string filename = argv[1];

 //Create a new DecisionTree instance
 DecisionTree dTree;
 
 //Set the node that the DecisionTree will use - different nodes may result in different decision boundaries
 //and some nodes may provide better accuracy than others on specific classification tasks
 //The current node options are:
 //- DecisionTreeClusterNode
 //- DecisionTreeThresholdNode
 dTree.setDecisionTreeNode( DecisionTreeClusterNode() );
 
 //Set the number of steps that will be used to choose the best splitting values
 //More steps will give you a better model, but will take longer to train
 dTree.setNumSplittingSteps( 1000 );
 
 //Set the maximum depth of the tree
 dTree.setMaxDepth( 10 );
 
 //Set the minimum number of samples allowed per node
 dTree.setMinNumSamplesPerNode( 10 );
 
 //Load some training data to train the classifier
 ClassificationData trainingData;
 
 if( !trainingData.load( filename ) ){
 cout << "Failed to load training data: " << filename << endl;
 return EXIT_FAILURE;
 }
 
 //Use 20% of the training dataset to create a test dataset
 ClassificationData testData = trainingData.split( 80 );
 
 //Train the classifier
 if( !dTree.train( trainingData ) ){
 cout << "Failed to train classifier!\n";
 return EXIT_FAILURE;
 }
 
 //Print the tree
 dTree.print();
 
 //Save the model to a file
 if( !dTree.save("DecisionTreeModel.grt") ){
 cout << "Failed to save the classifier model!\n";
 return EXIT_FAILURE;
 }
 
 //Load the model from a file
 if( !dTree.load("DecisionTreeModel.grt") ){
 cout << "Failed to load the classifier model!\n";
 return EXIT_FAILURE;
 }
 
 //Test the accuracy of the model on the test data
 double accuracy = 0;
 for(UINT i=0; i<testData.getNumSamples(); i++){
 //Get the i'th test sample
 UINT classLabel = testData[i].getClassLabel();
 VectorDouble inputVector = testData[i].getSample();
 
 //Perform a prediction using the classifier
 bool predictSuccess = dTree.predict( inputVector );
 
 if( !predictSuccess ){
 cout << "Failed to perform prediction for test sampel: " << i <<"\n";
 return EXIT_FAILURE;
 }
 
 //Get the predicted class label
 UINT predictedClassLabel = dTree.getPredictedClassLabel();
 VectorDouble classLikelihoods = dTree.getClassLikelihoods();
 VectorDouble classDistances = dTree.getClassDistances();
 
 //Update the accuracy
 if( classLabel == predictedClassLabel ) accuracy++;
 
 cout << "TestSample: " << i << " ClassLabel: " << classLabel << " PredictedClassLabel: " << predictedClassLabel << endl;
 }
 
 cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
 
 return EXIT_SUCCESS;
}