DecisionStump.cpp

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#define GRT_DLL_EXPORTS
#include "DecisionStump.h"

GRT_BEGIN_NAMESPACE
 
//Register the DecisionStump module with the WeakClassifier base class
RegisterWeakClassifierModule< DecisionStump > DecisionStump::registerModule("DecisionStump");
 
DecisionStump::DecisionStump(const UINT numRandomSplits){
 this->numRandomSplits = numRandomSplits;
 trained = false;
 numInputDimensions = 0;
 decisionFeatureIndex = 0;
 decisionValue = 0;
 direction = 0;
 weakClassifierType = "DecisionStump";
 trainingLog.setKey("[TRAINING DecisionStump]");
 warningLog.setKey("[WARNING DecisionStump]");
 errorLog.setKey("[ERROR DecisionStump]");
}
 
DecisionStump::~DecisionStump(){
 
}

DecisionStump::DecisionStump(const DecisionStump &rhs){
 *this = rhs;
}

DecisionStump& DecisionStump::operator=(const DecisionStump &rhs){
 if( this != &rhs ){
 this->decisionFeatureIndex = rhs.decisionFeatureIndex;
 this->decisionValue = rhs.decisionValue;
 this->direction = rhs.direction;
 this->numRandomSplits = rhs.numRandomSplits;
 this->copyBaseVariables( &rhs );
 }
 return *this;
}

bool DecisionStump::deepCopyFrom(const WeakClassifier *weakClassifer){
 if( weakClassifer == NULL ) return false;
 
 if( this->getWeakClassifierType() == weakClassifer->getWeakClassifierType() ){
 *this = *(DecisionStump*)weakClassifer;
 return true;
 }
 return false;
}

bool DecisionStump::train(ClassificationData &trainingData, VectorFloat &weights){
 
 trained = false;
 numInputDimensions = trainingData.getNumDimensions();
 
 //There should only be two classes in the dataset, the positive class (classLable==1) and the negative class (classLabel==2)
 if( trainingData.getNumClasses() != 2 ){
 errorLog << "train(ClassificationData &trainingData, VectorFloat &weights) - There should only be 2 classes in the training data, but there are : " << trainingData.getNumClasses() << std::endl;
 return false;
 }
 
 //There should be one weight for every training sample
 if( trainingData.getNumSamples() != weights.size() ){
 errorLog << "train(ClassificationData &trainingData, VectorFloat &weights) - There number of examples in the training data (" << trainingData.getNumSamples() << ") does not match the lenght of the weights vector (" << weights.getSize() << ")" << std::endl;
 return false;
 }
 
 //Pick the training sample to use as the stump feature
 const UINT M = trainingData.getNumSamples();
 UINT bestFeatureIndex = 0;
 Vector< MinMax > ranges = trainingData.getRanges();
 Float minError = grt_numeric_limits< Float >::max();
 Float minRange = 0;
 Float maxRange = 0;
 Float threshold = 0;
 Float bestThreshold = 0;
 Random random;
 
 for(UINT k=0; k<numRandomSplits; k++){
 
 //Randomly select a feature and a threshold
 UINT n = random.getRandomNumberInt(0,numInputDimensions);
 minRange = ranges[n].minValue;
 maxRange = ranges[n].maxValue;
 threshold = random.getRandomNumberUniform( minRange, maxRange );
 
 //Compute the error using the current threshold on the current input dimension
 //We need to check both sides of the threshold
 Float rhsError = 0;
 Float lhsError = 0;
 for(UINT i=0; i<M; i++){
 bool positiveClass = trainingData[ i ].getClassLabel() == WEAK_CLASSIFIER_POSITIVE_CLASS_LABEL;
 bool rhs = trainingData[ i ][ n ] >= threshold;
 bool lhs = trainingData[ i ][ n ] <= threshold;
 if( (rhs && !positiveClass) || (!rhs && positiveClass) ) rhsError += weights[ i ];
 if( (lhs && !positiveClass) || (!lhs && positiveClass) ) lhsError += weights[ i ];
 }
 
 //Check to see if either the rhsError or lhsError beats the minError, if so then store the results
 if( rhsError < minError ){
 minError = rhsError;
 bestFeatureIndex = n;
 bestThreshold = threshold;
 direction = 1; //1 means rhs
 }
 if( lhsError < minError ){
 minError = lhsError;
 bestFeatureIndex = n;
 bestThreshold = threshold;
 direction = 0; //0 means lhs
 }
 
 }
 
 decisionFeatureIndex = bestFeatureIndex;
 decisionValue = bestThreshold;
 trained = true;
 
 trainingLog << "Best Feature Index: " << decisionFeatureIndex << " Value: " << decisionValue << " Direction: " << direction << " Error: " << minError << std::endl;
 return true;
}

Float DecisionStump::predict(const VectorFloat &x){
 if( direction == 1){
 if( x[ decisionFeatureIndex ] >= decisionValue ) return 1;
 }else if( x[ decisionFeatureIndex ] <= decisionValue ) return 1;
 return -1;
}
 
bool DecisionStump::saveModelToFile( std::fstream &file ) const{
 
 if(!file.is_open())
 {
 errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 //Write the WeakClassifierType data
 file << "WeakClassifierType: " << weakClassifierType << std::endl;
 file << "Trained: "<< trained << std::endl;
 file << "NumInputDimensions: " << numInputDimensions << std::endl;
 
 //Write the DecisionStump data
 file << "DecisionFeatureIndex: " << decisionFeatureIndex << std::endl;
 file << "Direction: "<< direction << std::endl;
 file << "NumRandomSplits: " << numRandomSplits << std::endl;
 file << "DecisionValue: " << decisionValue << std::endl;
 
 //We don't need to close the file as the function that called this function should handle that
 return true;
}

bool DecisionStump::loadModelFromFile( std::fstream &file ){
 
 if(!file.is_open())
 {
 errorLog <<"loadModelFromFile(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 std::string word;
 
 file >> word;
 if( word != "WeakClassifierType:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read WeakClassifierType header!" << std::endl;
 return false;
 }
 file >> word;
 
 if( word != weakClassifierType ){
 errorLog <<"loadModelFromFile(fstream &file) - The weakClassifierType:" << word << " does not match: " << weakClassifierType << std::endl;
 return false;
 }
 
 file >> word;
 if( word != "Trained:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read Trained header!" << std::endl;
 return false;
 }
 file >> trained;
 
 file >> word;
 if( word != "NumInputDimensions:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read NumInputDimensions header!" << std::endl;
 return false;
 }
 file >> numInputDimensions;
 
 file >> word;
 if( word != "DecisionFeatureIndex:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read DecisionFeatureIndex header!" << std::endl;
 return false;
 }
 file >> decisionFeatureIndex;
 
 file >> word;
 if( word != "Direction:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read Direction header!" << std::endl;
 return false;
 }
 file >> direction;
 
 file >> word;
 if( word != "NumRandomSplits:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read NumRandomSplits header!" << std::endl;
 return false;
 }
 file >> numRandomSplits;
 
 file >> word;
 if( word != "DecisionValue:" ){
 errorLog <<"loadModelFromFile(fstream &file) - Failed to read DecisionValue header!" << std::endl;
 return false;
 }
 file >> decisionValue;
 
 //We don't need to close the file as the function that called this function should handle that
 return true;
}

void DecisionStump::print() const{
 std::cout << "Trained: " << trained;
 std::cout << "\tDecisionValue: " << decisionValue;
 std::cout << "\tDecisionFeatureIndex: " << decisionFeatureIndex;
 std::cout << "\tDirection: " << direction << std::endl;
}
 
UINT DecisionStump::getDecisionFeatureIndex() const{
 return decisionFeatureIndex;
}

UINT DecisionStump::getDirection() const{
 return direction;
}

UINT DecisionStump::getNumRandomSplits() const{
 return numRandomSplits;
}

Float DecisionStump::getDecisionValue() const{
 return decisionValue;
}

GRT_END_NAMESPACE