DecisionTreeTripleFeatureNode.cpp

#define GRT_DLL_EXPORTS
#include "DecisionTreeTripleFeatureNode.h"

GRT_BEGIN_NAMESPACE
 
//Register the DecisionTreeTripleFeatureNode module with the Node base class
RegisterNode< DecisionTreeTripleFeatureNode > DecisionTreeTripleFeatureNode::registerModule("DecisionTreeTripleFeatureNode");
 
DecisionTreeTripleFeatureNode::DecisionTreeTripleFeatureNode() : DecisionTreeNode("DecisionTreeTripleFeatureNode") {
 clear();
}

DecisionTreeTripleFeatureNode::~DecisionTreeTripleFeatureNode(){
 clear();
}

bool DecisionTreeTripleFeatureNode::predict_(VectorFloat &x) {

 if( (x[ featureIndexA ] - x[ featureIndexB ]) >= (x[ featureIndexC ] - x[ featureIndexB ]) ) return true;

 return false;
}

bool DecisionTreeTripleFeatureNode::clear(){
 
 //Call the base class clear function
 DecisionTreeNode::clear();
 
 featureIndexA = 0;
 featureIndexB = 0;
 featureIndexC = 0;
 
 return true;
}

bool DecisionTreeTripleFeatureNode::print() const{
 
 std::ostringstream stream;
 
 if( getModel( stream ) ){
 std::cout << stream.str();
 return true;
 }
 
 return false;
}
 
bool DecisionTreeTripleFeatureNode::getModel( std::ostream &stream ) const{

 std::string tab = "";
 for(UINT i=0; i<depth; i++) tab += "\t";
 
 stream << tab << "depth: " << depth;
 stream << " nodeSize: " << nodeSize;
 stream << " featureIndexA: " << featureIndexA;
 stream << " featureIndexB: " << featureIndexB;
 stream << " featureIndexC: " << featureIndexC;
 stream << " isLeafNode: " << isLeafNode << std::endl;

 stream << tab << "ClassProbabilities: ";
 for(UINT i=0; i<classProbabilities.size(); i++){
 stream << classProbabilities[i] << "\t";
 }
 stream << std::endl;
 
 if( leftChild != NULL ){
 stream << tab << "LeftChild: " << std::endl;
 leftChild->getModel( stream );
 }
 
 if( rightChild != NULL ){
 stream << tab << "RightChild: " << std::endl;
 rightChild->getModel( stream );
 }
 
 return true;
}

Node* DecisionTreeTripleFeatureNode::deepCopy() const{
 
 DecisionTreeTripleFeatureNode *node = new DecisionTreeTripleFeatureNode;
 
 if( node == NULL ){
 return NULL;
 }
 
 //Copy this node into the node
 node->depth = depth;
 node->isLeafNode = isLeafNode;
 node->nodeID = nodeID;
 node->predictedNodeID = predictedNodeID;
 node->nodeSize = nodeSize;
 node->featureIndexA = featureIndexA;
 node->featureIndexB = featureIndexB;
 node->featureIndexC = featureIndexC;
 node->classProbabilities = classProbabilities;
 
 //Recursively deep copy the left child
 if( leftChild ){
 node->leftChild = leftChild->deepCopy();
 node->leftChild->setParent( node );
 }
 
 //Recursively deep copy the right child
 if( rightChild ){
 node->rightChild = rightChild->deepCopy();
 node->rightChild->setParent( node );
 }
 
 return dynamic_cast< Node* >( node );
}

UINT DecisionTreeTripleFeatureNode::getFeatureIndexA() const{
 return featureIndexA;
}

UINT DecisionTreeTripleFeatureNode::getFeatureIndexB() const{
 return featureIndexB;
}

UINT DecisionTreeTripleFeatureNode::getFeatureIndexC() const{
 return featureIndexC;
}

bool DecisionTreeTripleFeatureNode::set(const UINT nodeSize,const UINT featureIndexA,const UINT featureIndexB,const UINT featureIndexC,const VectorFloat &classProbabilities){
 this->nodeSize = nodeSize;
 this->featureIndexA = featureIndexA;
 this->featureIndexB = featureIndexB;
 this->featureIndexC = featureIndexC;
 this->classProbabilities = classProbabilities;
 return true;
}

bool DecisionTreeTripleFeatureNode::computeBestSplitBestIterativeSplit( const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError ){
 return computeSplit( numSplittingSteps, trainingData, features, classLabels, featureIndex, minError);
}

bool DecisionTreeTripleFeatureNode::computeBestSplitBestRandomSplit( const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError ){
 return computeSplit( numSplittingSteps, trainingData, features, classLabels, featureIndex, minError);
}

bool DecisionTreeTripleFeatureNode::computeSplit( const UINT &numSplittingSteps, const ClassificationData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &minError ){

 const UINT M = trainingData.getNumSamples();
 const UINT N = features.getSize();
 const UINT K = classLabels.getSize();
 
 if( N == 0 ) return false;
 
 minError = grt_numeric_limits< Float >::max();
 Random random;
 UINT bestFeatureIndexA = 0;
 UINT bestFeatureIndexB = 0;
 UINT bestFeatureIndexC = 0;
 Float error = 0;
 Float giniIndexL = 0;
 Float giniIndexR = 0;
 Float weightL = 0;
 Float weightR = 0;
 Vector< UINT > groupIndex(M);
 VectorFloat groupCounter(2,0);
 Vector< MinMax > ranges = trainingData.getRanges();
 MatrixFloat classProbabilities(K,2);
 MatrixFloat data(M,1); //This will store our temporary data for each dimension
 
 //Randomly select which features we want to use
 UINT numRandomFeatures = numSplittingSteps > N ? N : numSplittingSteps;
 Vector< UINT > randomFeatures = random.getRandomSubset( 0, N, numRandomFeatures );

 //Loop over each random feature and try and find the best split point
 for(UINT n=0; n<numRandomFeatures; n++){
 
 //Randomly select 3 features to use
 featureIndexB = features[ randomFeatures[n] ]; //B is the central feature
 featureIndexA = features[ randomFeatures[ random.getRandomNumberInt(0,numRandomFeatures) ] ];
 featureIndexC = features[ randomFeatures[ random.getRandomNumberInt(0,numRandomFeatures) ] ];
 
 //Iterate over each sample and work out if it should be in the lhs (0) or rhs (1) group based on the current threshold
 groupCounter[0] = groupCounter[1] = 0;
 classProbabilities.setAllValues(0);
 for(UINT i=0; i<M; i++){
 groupIndex[i] = predict( trainingData[i].getSample() ) ? 1 : 0;
 groupCounter[ groupIndex[i] ]++;
 classProbabilities[ getClassLabelIndexValue(trainingData[i].getClassLabel(),classLabels) ][ groupIndex[i] ]++;
 }
 
 //Compute the class probabilities for the lhs group and rhs group
 for(UINT k=0; k<K; k++){
 classProbabilities[k][0] = groupCounter[0]>0 ? classProbabilities[k][0]/groupCounter[0] : 0;
 classProbabilities[k][1] = groupCounter[1]>0 ? classProbabilities[k][1]/groupCounter[1] : 0;
 }
 
 //Compute the Gini index for the lhs and rhs groups
 giniIndexL = giniIndexR = 0;
 for(UINT k=0; k<K; k++){
 giniIndexL += classProbabilities[k][0] * (1.0-classProbabilities[k][0]);
 giniIndexR += classProbabilities[k][1] * (1.0-classProbabilities[k][1]);
 }
 weightL = groupCounter[0]/M;
 weightR = groupCounter[1]/M;
 error = (giniIndexL*weightL) + (giniIndexR*weightR);
 
 //Store the best threshold and feature index
 if( error < minError ){
 minError = error;
 bestFeatureIndexA = featureIndexA;
 bestFeatureIndexB = featureIndexB;
 bestFeatureIndexC = featureIndexC;
 }
 }

 trainingLog << "Best features indexs: [" << bestFeatureIndexA << "," << bestFeatureIndexB << "," << bestFeatureIndexC << "] Min Error: " << minError << std::endl;
 
 //Set the best feature index that will be returned to the DecisionTree that called this function
 featureIndex = bestFeatureIndexB;
 
 //Store the node size, feature indexs and class probabilities for this node
 set(M,bestFeatureIndexA,bestFeatureIndexB,bestFeatureIndexC,trainingData.getClassProbabilities(classLabels));

 return true;
}

bool DecisionTreeTripleFeatureNode::saveParametersToFile( std::fstream &file ) const{
 
 if( !file.is_open() )
 {
 errorLog << "saveParametersToFile(fstream &file) - File is not open!" << std::endl;
 return false;
 }
 
 //Save the DecisionTreeNode parameters
 if( !DecisionTreeNode::saveParametersToFile( file ) ){
 errorLog << "saveParametersToFile(fstream &file) - Failed to save DecisionTreeNode parameters to file!" << std::endl;
 return false;
 }
 
 //Save the custom DecisionTreeThresholdNode parameters
 file << "FeatureIndexA: " << featureIndexA << std::endl;
 file << "FeatureIndexB: " << featureIndexB << std::endl;
 file << "FeatureIndexC: " << featureIndexC << std::endl;
 
 return true;
}

bool DecisionTreeTripleFeatureNode::loadParametersFromFile( std::fstream &file ){
 
 if(!file.is_open())
 {
 errorLog << "loadParametersFromFile(fstream &file) - File is not open!" << std::endl;
 return false;
 }
 
 //Load the DecisionTreeNode parameters
 if( !DecisionTreeNode::loadParametersFromFile( file ) ){
 errorLog << "loadParametersFromFile(fstream &file) - Failed to load DecisionTreeNode parameters from file!" << std::endl;
 return false;
 }
 
 std::string word;
 //Load the custom DecisionTreeThresholdNode Parameters
 file >> word;
 if( word != "FeatureIndexA:" ){
 errorLog << "loadParametersFromFile(fstream &file) - Failed to find FeatureIndexA header!" << std::endl;
 return false;
 }
 file >> featureIndexA;
 
 file >> word;
 if( word != "FeatureIndexB:" ){
 errorLog << "loadParametersFromFile(fstream &file) - Failed to find FeatureIndexB header!" << std::endl;
 return false;
 }
 file >> featureIndexB;

 file >> word;
 if( word != "FeatureIndexC:" ){
 errorLog << "loadParametersFromFile(fstream &file) - Failed to find FeatureIndexC header!" << std::endl;
 return false;
 }
 file >> featureIndexC;
 
 return true;
}

GRT_END_NAMESPACE