DecisionTreeThresholdNode.cpp

#define GRT_DLL_EXPORTS
#include "DecisionTreeThresholdNode.h"

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

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

bool DecisionTreeThresholdNode::predict_(VectorFloat &x) {

 if( x[ featureIndex ] >= threshold ) return true;
 
 return false;
}

bool DecisionTreeThresholdNode::clear(){
 
 //Call the base class clear function
 DecisionTreeNode::clear();
 
 featureIndex = 0;
 threshold = 0;
 
 return true;
}

bool DecisionTreeThresholdNode::print() const{
 
 std::ostringstream stream;
 
 if( getModel( stream ) ){
 std::cout << stream.str();
 return true;
 }
 
 return false;
}
 
bool DecisionTreeThresholdNode::getModel( std::ostream &stream ) const{
 
 std::string tab = "";
 for(UINT i=0; i<depth; i++) tab += "\t";
 
 stream << tab << "depth: " << depth << " nodeSize: " << nodeSize << " featureIndex: " << featureIndex << " threshold " << threshold << " 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* DecisionTreeThresholdNode::deepCopy() const{
 
 DecisionTreeThresholdNode *node = new DecisionTreeThresholdNode;
 
 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->featureIndex = featureIndex;
 node->threshold = threshold;
 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 DecisionTreeThresholdNode::getFeatureIndex() const{
 return featureIndex;
}

Float DecisionTreeThresholdNode::getThreshold() const{
 return threshold;
}

bool DecisionTreeThresholdNode::set(const UINT nodeSize,const UINT featureIndex,const Float threshold,const VectorFloat &classProbabilities){
 this->nodeSize = nodeSize;
 this->featureIndex = featureIndex;
 this->threshold = threshold;
 this->classProbabilities = classProbabilities;
 return true;
}

bool DecisionTreeThresholdNode::computeBestSplitBestIterativeSplit( 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();
 UINT bestFeatureIndex = 0;
 Float bestThreshold = 0;
 Float error = 0;
 Float minRange = 0;
 Float maxRange = 0;
 Float step = 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);
 
 //Loop over each feature and try and find the best split point
 for(UINT n=0; n<N; n++){
 minRange = ranges[n].minValue;
 maxRange = ranges[n].maxValue;
 step = (maxRange-minRange)/Float(numSplittingSteps);
 threshold = minRange;
 featureIndex = features[n];
 while( threshold <= maxRange ){
 
 //Iterate over each sample and work out if it should be in the lhs (0) or rhs (1) group
 groupCounter[0] = groupCounter[1] = 0;
 classProbabilities.setAllValues(0);
 for(UINT i=0; i<M; i++){
 groupIndex[i] = trainingData[ i ][ featureIndex ] >= threshold ? 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;
 bestThreshold = threshold;
 bestFeatureIndex = featureIndex;
 }
 
 //Update the threshold
 threshold += step;
 }
 }
 
 //Set the best feature index that will be returned to the DecisionTree that called this function
 featureIndex = bestFeatureIndex;
 
 //Store the node size, feature index, best threshold and class probabilities for this node
 set(M,featureIndex,bestThreshold,trainingData.getClassProbabilities(classLabels));
 
 return true;
}

bool DecisionTreeThresholdNode::computeBestSplitBestRandomSplit( 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 = (UINT)features.size();
 const UINT K = (UINT)classLabels.size();
 
 if( N == 0 ) return false;
 
 minError = grt_numeric_limits< Float >::max();
 UINT bestFeatureIndex = 0;
 Float bestThreshold = 0;
 Float error = 0;
 Float giniIndexL = 0;
 Float giniIndexR = 0;
 Float weightL = 0;
 Float weightR = 0;
 Random random;
 Vector< UINT > groupIndex(M);
 VectorFloat groupCounter(2,0);
 
 MatrixFloat classProbabilities(K,2);

 //Loop over each feature and try and find the best split point
 UINT m,n;
 const UINT numFeatures = features.getSize();
 for(m=0; m<numSplittingSteps; m++){
 //Chose a random feature
 n = random.getRandomNumberInt(0,numFeatures);
 featureIndex = features[n];
 
 //Randomly choose the threshold, the threshold is based on a randomly selected sample with some random scaling
 threshold = trainingData[ random.getRandomNumberInt(0,M) ][ featureIndex ] * random.getRandomNumberUniform(0.8,1.2);
 
 //Iterate over each sample and work out if it should be in the lhs (0) or rhs (1) group
 groupCounter[0] = groupCounter[1] = 0;
 classProbabilities.setAllValues(0);
 for(UINT i=0; i<M; i++){
 groupIndex[i] = trainingData[ i ][ featureIndex ] >= threshold ? 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;
 bestThreshold = threshold;
 bestFeatureIndex = featureIndex;
 }
 }
 
 //Set the best feature index that will be returned to the DecisionTree that called this function
 featureIndex = bestFeatureIndex;
 
 //Store the node size, feature index, best threshold and class probabilities for this node
 set(M,featureIndex,bestThreshold,trainingData.getClassProbabilities(classLabels));
 
 return true;
}

bool DecisionTreeThresholdNode::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 << "FeatureIndex: " << featureIndex << std::endl;
 file << "Threshold: " << threshold << std::endl;
 
 return true;
}

bool DecisionTreeThresholdNode::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 != "FeatureIndex:" ){
 errorLog << "loadParametersFromFile(fstream &file) - Failed to find FeatureIndex header!" << std::endl;
 return false;
 }
 file >> featureIndex;
 
 file >> word;
 if( word != "Threshold:" ){
 errorLog << "loadParametersFromFile(fstream &file) - Failed to find Threshold header!" << std::endl;
 return false;
 }
 file >> threshold;
 
 return true;
}

GRT_END_NAMESPACE