void BoostedClassifier::train(const vector<TrainingExample*>& examples) {
if(examples.size()==0) return;
assert(m_trees.size()==0);
size_t m = examples.size();
vector<double> distribs(m,1.0/m);
m_sum_alphas = 0;
for(size_t b=0; b<m_num_max_trees; b++) {
DecisionTree* hb = new DecisionTree(m_label);
hb->train(examples, distribs);
double epsb = 0;
size_t num_misclassified=0;
for(size_t i=0; i<m; i++) {
double predictprob= hb->predict(*examples[i]);
bool prediction = (predictprob>0.5)?true:false;
// cerr << "Actual label: " << egs_for_tree[i]->getLabel() << endl;
// cerr << ", Predicted: " << m_label << " with prob " << predictprob << endl;
bool actual = isLabelEqual(examples[i]->getLabel(), m_label);
if(prediction!=actual) {
epsb+=distribs[i];
num_misclassified++;
}
}
// cerr << "Number misclassified: " << num_misclassified << " of " << m << ", my label: " << m_label << endl;
// cerr << "\tEpsb: " << epsb << endl;
double epsilon = 0.001;
if(epsb==0) {
epsb=epsilon;
} else if(epsb==1.0) {
epsb=1-epsilon;
}
double alphab = 0.5*log((1-epsb)/epsb)/log(2.0);
double z = 0.0;
for(size_t i=0; i<m; i++) {
double predictprob= hb->predict(*examples[i]);
// cerr << "My tree label: " << m_label << ", actual label: " << egs_for_tree[i]->getLabel()<< ", prediction probability: " << predictprob << endl;
bool prediction = (predictprob>0.5)?true:false;
bool actual = isLabelEqual(examples[i]->getLabel(),m_label);
if(prediction!=actual) {
// if incorrect drum up...
distribs[i] = distribs[i]*exp(alphab);
} else {
// if correct drum down...
distribs[i] = distribs[i]*exp(-alphab);
}
z +=distribs[i];
}
// cerr << "z: " << z << endl;
for(size_t i=0; i<m; i++) {
distribs[i] = distribs[i]/z;
}
m_trees.push_back(hb);
m_tree_alpha.push_back(alphab);
m_sum_alphas += alphab;
// cerr << "Trained tree with alphab: " << alphab <<endl;
} // for each tree
}
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;
}