RegressionTree.cpp

/*
GRT MIT License
Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software 
and associated documentation files (the "Software"), to deal in the Software without restriction, 
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, 
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, 
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial 
portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, 
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*/

#include "RegressionTree.h"

GRT_BEGIN_NAMESPACE
 
//Register the RegressionTreeNode with the Node base class
RegisterNode< RegressionTreeNode > RegressionTreeNode::registerModule("RegressionTreeNode");

//Register the RegressionTree module with the Regressifier base class
RegisterRegressifierModule< RegressionTree > RegressionTree::registerModule("RegressionTree");

RegressionTree::RegressionTree(const UINT numSplittingSteps,const UINT minNumSamplesPerNode,const UINT maxDepth,const bool removeFeaturesAtEachSpilt,const UINT trainingMode,const bool useScaling,const Float minRMSErrorPerNode)
{
 tree = NULL;
 this->numSplittingSteps = numSplittingSteps;
 this->minNumSamplesPerNode = minNumSamplesPerNode;
 this->maxDepth = maxDepth;
 this->removeFeaturesAtEachSpilt = removeFeaturesAtEachSpilt;
 this->trainingMode = trainingMode;
 this->useScaling = useScaling;
 this->minRMSErrorPerNode = minRMSErrorPerNode;
 Regressifier::classType = "RegressionTree";
 regressifierType = Regressifier::classType;
 Regressifier::debugLog.setProceedingText("[DEBUG RegressionTree]");
 Regressifier::errorLog.setProceedingText("[ERROR RegressionTree]");
 Regressifier::trainingLog.setProceedingText("[TRAINING RegressionTree]");
 Regressifier::warningLog.setProceedingText("[WARNING RegressionTree]");
 
}
 
RegressionTree::RegressionTree(const RegressionTree &rhs){
 tree = NULL;
 Regressifier::classType = "RegressionTree";
 regressifierType = Regressifier::classType;
 Regressifier::debugLog.setProceedingText("[DEBUG RegressionTree]");
 Regressifier::errorLog.setProceedingText("[ERROR RegressionTree]");
 Regressifier::trainingLog.setProceedingText("[TRAINING RegressionTree]");
 Regressifier::warningLog.setProceedingText("[WARNING RegressionTree]");
 *this = rhs;
}

RegressionTree::~RegressionTree(void)
{
 clear();
}
 
RegressionTree& RegressionTree::operator=(const RegressionTree &rhs){
 if( this != &rhs ){
 //Clear this tree
 this->clear();
 
 if( rhs.getTrained() ){
 //Deep copy the tree
 this->tree = dynamic_cast< RegressionTreeNode* >( rhs.deepCopyTree() );
 }
 
 this->numSplittingSteps = rhs.numSplittingSteps;
 this->minNumSamplesPerNode = rhs.minNumSamplesPerNode;
 this->maxDepth = rhs.maxDepth;
 this->removeFeaturesAtEachSpilt = rhs.removeFeaturesAtEachSpilt;
 this->trainingMode = rhs.trainingMode;
 this->minRMSErrorPerNode = rhs.minRMSErrorPerNode;

 //Copy the base variables
 copyBaseVariables( (Regressifier*)&rhs );
 }
 return *this;
}

bool RegressionTree::deepCopyFrom(const Regressifier *regressifier){
 
 if( regressifier == NULL ) return false;
 
 if( this->getRegressifierType() == regressifier->getRegressifierType() ){
 
 RegressionTree *ptr = (RegressionTree*)regressifier;
 
 //Clear this tree
 this->clear();
 
 if( ptr->getTrained() ){
 //Deep copy the tree
 this->tree = dynamic_cast< RegressionTreeNode* >( ptr->deepCopyTree() );
 }
 
 this->numSplittingSteps = ptr->numSplittingSteps;
 this->minNumSamplesPerNode = ptr->minNumSamplesPerNode;
 this->maxDepth = ptr->maxDepth;
 this->removeFeaturesAtEachSpilt = ptr->removeFeaturesAtEachSpilt;
 this->trainingMode = ptr->trainingMode;
 this->minRMSErrorPerNode = ptr->minRMSErrorPerNode;
 
 //Copy the base variables
 return copyBaseVariables( regressifier );
 }
 return false;
}

bool RegressionTree::train_(RegressionData &trainingData){
 
 //Clear any previous model
 clear();
 
 const unsigned int M = trainingData.getNumSamples();
 const unsigned int N = trainingData.getNumInputDimensions();
 const unsigned int T = trainingData.getNumTargetDimensions();
 
 if( M == 0 ){
 Regressifier::errorLog << "train_(RegressionData &trainingData) - Training data has zero samples!" << std::endl;
 return false;
 }
 
 numInputDimensions = N;
 numOutputDimensions = T;
 inputVectorRanges = trainingData.getInputRanges();
 targetVectorRanges = trainingData.getTargetRanges();
 
 //Scale the training data if needed
 if( useScaling ){
 //Scale the training data between 0 and 1
 trainingData.scale(0, 1);
 }
 
 //Setup the valid features - at this point all features can be used
 Vector< UINT > features(N);
 for(UINT i=0; i<N; i++){
 features[i] = i;
 }
 
 //Build the tree
 UINT nodeID = 0;
 tree = buildTree( trainingData, NULL, features, nodeID );
 
 if( tree == NULL ){
 clear();
 Regressifier::errorLog << "train_(RegressionData &trainingData) - Failed to build tree!" << std::endl;
 return false;
 }
 
 //Flag that the algorithm has been trained
 trained = true;
 
 return true;
}

bool RegressionTree::predict_(VectorFloat &inputVector){
 
 if( !trained ){
 Regressifier::errorLog << "predict_(VectorFloat &inputVector) - Model Not Trained!" << std::endl;
 return false;
 }
 
 if( tree == NULL ){
 Regressifier::errorLog << "predict_(VectorFloat &inputVector) - Tree pointer is null!" << std::endl;
 return false;
 }
 
 if( inputVector.size() != numInputDimensions ){
 Regressifier::errorLog << "predict_(VectorFloat &inputVector) - The size of the input Vector (" << inputVector.size() << ") does not match the num features in the model (" << numInputDimensions << std::endl;
 return false;
 }
 
 if( useScaling ){
 for(UINT n=0; n<numInputDimensions; n++){
 inputVector[n] = scale(inputVector[n], inputVectorRanges[n].minValue, inputVectorRanges[n].maxValue, 0, 1);
 }
 }
 
 if( !tree->predict( inputVector, regressionData ) ){
 Regressifier::errorLog << "predict_(VectorFloat &inputVector) - Failed to predict!" << std::endl;
 return false;
 }
 
 return true;
}
 
bool RegressionTree::clear(){
 
 //Clear the Classifier variables
 Regressifier::clear();
 
 if( tree != NULL ){
 tree->clear();
 delete tree;
 tree = NULL;
 }
 
 return true;
}

bool RegressionTree::print() const{
 if( tree != NULL )
 return tree->print();
 return false;
}
 
bool RegressionTree::saveModelToFile( std::fstream &file ) const{
 
 if(!file.is_open())
 {
 Regressifier::errorLog <<"saveModelToFile(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 //Write the header info
 file << "GRT_REGRESSION_TREE_MODEL_FILE_V1.0\n";
 
 //Write the classifier settings to the file
 if( !Regressifier::saveBaseSettingsToFile(file) ){
 Regressifier::errorLog <<"saveModelToFile(fstream &file) - Failed to save classifier base settings to file!" << std::endl;
 return false;
 }
 
 file << "NumSplittingSteps: " << numSplittingSteps << std::endl;
 file << "MinNumSamplesPerNode: " << minNumSamplesPerNode << std::endl;
 file << "MaxDepth: " << maxDepth << std::endl;
 file << "RemoveFeaturesAtEachSpilt: " << removeFeaturesAtEachSpilt << std::endl;
 file << "TrainingMode: " << trainingMode << std::endl;
 file << "TreeBuilt: " << (tree != NULL ? 1 : 0) << std::endl;
 
 if( tree != NULL ){
 file << "Tree:\n";
 if( !tree->saveToFile( file ) ){
 Regressifier::errorLog << "saveModelToFile(fstream &file) - Failed to save tree to file!" << std::endl;
 return false;
 }
 }
 
 return true;
}
 
bool RegressionTree::loadModelFromFile( std::fstream &file ){
 
 clear();
 
 if(!file.is_open())
 {
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not open file to load model" << std::endl;
 return false;
 }
 
 std::string word;
 file >> word;
 
 //Find the file type header
 if(word != "GRT_REGRESSION_TREE_MODEL_FILE_V1.0"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find Model File Header" << std::endl;
 return false;
 }
 
 //Load the base settings from the file
 if( !Regressifier::loadBaseSettingsFromFile(file) ){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Failed to load base settings from file!" << std::endl;
 return false;
 }
 
 file >> word;
 if(word != "NumSplittingSteps:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the NumSplittingSteps!" << std::endl;
 return false;
 }
 file >> numSplittingSteps;
 
 file >> word;
 if(word != "MinNumSamplesPerNode:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the MinNumSamplesPerNode!" << std::endl;
 return false;
 }
 file >> minNumSamplesPerNode;
 
 file >> word;
 if(word != "MaxDepth:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the MaxDepth!" << std::endl;
 return false;
 }
 file >> maxDepth;
 
 file >> word;
 if(word != "RemoveFeaturesAtEachSpilt:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the RemoveFeaturesAtEachSpilt!" << std::endl;
 return false;
 }
 file >> removeFeaturesAtEachSpilt;
 
 file >> word;
 if(word != "TrainingMode:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the TrainingMode!" << std::endl;
 return false;
 }
 file >> trainingMode;
 
 file >> word;
 if(word != "TreeBuilt:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the TreeBuilt!" << std::endl;
 return false;
 }
 file >> trained;
 
 if( trained ){
 file >> word;
 if(word != "Tree:"){
 Regressifier::errorLog << "loadModelFromFile(string filename) - Could not find the Tree!" << std::endl;
 return false;
 }
 
 //Create a new tree
 tree = new RegressionTreeNode;
 
 if( tree == NULL ){
 clear();
 Regressifier::errorLog << "loadModelFromFile(fstream &file) - Failed to create new RegressionTreeNode!" << std::endl;
 return false;
 }
 
 tree->setParent( NULL );
 if( !tree->loadFromFile( file ) ){
 clear();
 Regressifier::errorLog << "loadModelFromFile(fstream &file) - Failed to load tree from file!" << std::endl;
 return false;
 }
 }
 
 return true;
}
 
RegressionTreeNode* RegressionTree::deepCopyTree() const{
 
 if( tree == NULL ){
 return NULL;
 }

 return (RegressionTreeNode*)tree->deepCopyNode();
}

const RegressionTreeNode* RegressionTree::getTree() const{
 return dynamic_cast< RegressionTreeNode* >( tree );
}
 
Float RegressionTree::getMinRMSErrorPerNode() const{
 return minRMSErrorPerNode;
}

bool RegressionTree::setMinRMSErrorPerNode(const Float minRMSErrorPerNode){
 this->minRMSErrorPerNode = minRMSErrorPerNode;
 return true;
}
 
RegressionTreeNode* RegressionTree::buildTree(const RegressionData &trainingData,RegressionTreeNode *parent,Vector< UINT > features,UINT nodeID){
 
 const UINT M = trainingData.getNumSamples();
 const UINT N = trainingData.getNumInputDimensions();
 const UINT T = trainingData.getNumTargetDimensions();
 VectorFloat regressionData(T);
 
 //Update the nodeID
 
 //Get the depth
 UINT depth = 0;
 
 if( parent != NULL )
 depth = parent->getDepth() + 1;
 
 //If there are no training data then return NULL
 if( trainingData.getNumSamples() == 0 )
 return NULL;
 
 //Create the new node
 RegressionTreeNode *node = new RegressionTreeNode;
 
 if( node == NULL )
 return NULL;
 
 //Set the parent
 node->initNode( parent, depth, nodeID );
 
 //If there are no features left then create a leaf node and return
 if( features.size() == 0 || M < minNumSamplesPerNode || depth >= maxDepth ){
 
 //Flag that this is a leaf node
 node->setIsLeafNode( true );
 
 //Compute the regression data that will be stored at this node
 computeNodeRegressionData( trainingData, regressionData );
 
 //Set the node
 node->set( trainingData.getNumSamples(), 0, 0, regressionData );
 
 Regressifier::trainingLog << "Reached leaf node. Depth: " << depth << " NumSamples: " << trainingData.getNumSamples() << std::endl;
 
 return node;
 }
 
 //Compute the best spilt point
 UINT featureIndex = 0;
 Float threshold = 0;
 Float minError = 0;
 if( !computeBestSpilt( trainingData, features, featureIndex, threshold, minError ) ){
 delete node;
 return NULL;
 }
 
 Regressifier::trainingLog << "Depth: " << depth << " FeatureIndex: " << featureIndex << " Threshold: " << threshold << " MinError: " << minError << std::endl;
 
 //If the minError is below the minRMSError then create a leaf node and return
 if( minError <= minRMSErrorPerNode ){
 //Compute the regression data that will be stored at this node
 computeNodeRegressionData( trainingData, regressionData );
 
 //Set the node
 node->set( trainingData.getNumSamples(), featureIndex, threshold, regressionData );
 
 Regressifier::trainingLog << "Reached leaf node. Depth: " << depth << " NumSamples: " << M << std::endl;
 
 return node;
 }
 
 //Set the node
 node->set( trainingData.getNumSamples(), featureIndex, threshold, regressionData );
 
 //Remove the selected feature so we will not use it again
 if( removeFeaturesAtEachSpilt ){
 for(UINT i=0; i<features.getSize(); i++){
 if( features[i] == featureIndex ){
 features.erase( features.begin()+i );
 break;
 }
 }
 }
 
 //Split the data
 RegressionData lhs(N,T);
 RegressionData rhs(N,T);
 
 for(UINT i=0; i<M; i++){
 if( node->predict( trainingData[i].getInputVector() ) ){
 rhs.addSample(trainingData[i].getInputVector(), trainingData[i].getTargetVector());
 }else lhs.addSample(trainingData[i].getInputVector(), trainingData[i].getTargetVector());
 }
 
 //Run the recursive tree building on the children
 node->setLeftChild( buildTree( lhs, node, features, nodeID ) );
 node->setRightChild( buildTree( rhs, node, features, nodeID ) );
 
 return node;
}
 
bool RegressionTree::computeBestSpilt( const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError ){
 
 switch( trainingMode ){
 case BEST_ITERATIVE_SPILT:
 return computeBestSpiltBestIterativeSpilt( trainingData, features, featureIndex, threshold, minError );
 break;
 case BEST_RANDOM_SPLIT:
 //return computeBestSpiltBestRandomSpilt( trainingData, features, featureIndex, threshold, minError );
 break;
 default:
 Regressifier::errorLog << "Uknown trainingMode!" << std::endl;
 return false;
 break;
 }
 
 return false;
}
 
bool RegressionTree::computeBestSpiltBestIterativeSpilt( const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError ){
 
 const UINT M = trainingData.getNumSamples();
 const UINT N = (UINT)features.size();
 
 if( N == 0 ) return false;
 
 minError = grt_numeric_limits< Float >::max();
 UINT bestFeatureIndex = 0;
 UINT groupID = 0;
 Float bestThreshold = 0;
 Float error = 0;
 Float minRange = 0;
 Float maxRange = 0;
 Float step = 0;
 Vector< UINT > groupIndex(M);
 VectorFloat groupCounter(2,0);
 VectorFloat groupMean(2,0);
 VectorFloat groupMSE(2,0);
 Vector< MinMax > ranges = trainingData.getInputRanges();
 
 //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 what group it falls into
 for(UINT i=0; i<M; i++){
 groupID = trainingData[i].getInputVector()[featureIndex] >= threshold ? 1 : 0;
 groupIndex[i] = groupID;
 groupMean[ groupID ] += trainingData[i].getInputVector()[featureIndex];
 groupCounter[ groupID ]++;
 }
 groupMean[0] /= groupCounter[0] > 0 ? groupCounter[0] : 1;
 groupMean[1] /= groupCounter[1] > 0 ? groupCounter[1] : 1;
 
 //Compute the MSE for each group
 for(UINT i=0; i<M; i++){
 groupMSE[ groupIndex[i] ] += grt_sqr( groupMean[ groupIndex[i] ] - trainingData[ i ].getInputVector()[features[n]] );
 }
 groupMSE[0] /= groupCounter[0] > 0 ? groupCounter[0] : 1;
 groupMSE[1] /= groupCounter[1] > 0 ? groupCounter[1] : 1;
 
 error = sqrt( groupMSE[0] + groupMSE[1] );
 
 //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 and threshold
 featureIndex = bestFeatureIndex;
 threshold = bestThreshold;
 
 return true;
}
 
 /*
bool RegressionTree::computeBestSpiltBestRandomSpilt( const RegressionData &trainingData, const Vector< UINT > &features, const Vector< UINT > &classLabels, UINT &featureIndex, Float &threshold, 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 = 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;
 Vector< UINT > groupIndex(M);
 VectorFloat groupCounter(2,0);
 Vector< MinMax > ranges = trainingData.getRanges();
 
 MatrixDouble classProbabilities(K,2);
 
 //Loop over each feature and try and find the best split point
 for(UINT n=0; n<N; n++){
 for(UINT m=0; m<numSplittingSteps; m++){
 //Randomly choose the threshold
 threshold = random.getRandomNumberUniform(ranges[n].minValue,ranges[n].maxValue);
 
 //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 ][ features[n] ] >= threshold ? 1 : 0;
 groupCounter[ groupIndex[i] ]++;
 classProbabilities[ getClassLabelIndexValue(trainingData[i].getClassLabel()) ][ 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 = n;
 }
 }
 }
 
 //Set the best feature index and threshold
 featureIndex = bestFeatureIndex;
 threshold = bestThreshold;
 
 return true;
}
 
*/
 
 //Compute the regression data that will be stored at this node
bool RegressionTree::computeNodeRegressionData( const RegressionData &trainingData, VectorFloat &regressionData ){
 
 const UINT M = trainingData.getNumSamples();
 const UINT N = trainingData.getNumInputDimensions();
 const UINT T = trainingData.getNumTargetDimensions();
 
 if( M == 0 ){
 Regressifier::errorLog << "computeNodeRegressionData(...) - Failed to compute regression data, there are zero training samples!" << std::endl;
 return false;
 }
 
 //Make sure the regression data is the correct size
 regressionData.clear();
 regressionData.resize( T, 0 );
 
 //The regression data at this node is simply an average over all the training data at this node
 for(unsigned int j=0; j<N; j++){
 for(unsigned int i=0; i<M; i++){
 regressionData[j] += trainingData[i].getTargetVector()[j];
 }
 regressionData[j] /= M;
 }
 
 return true;
}
 
GRT_END_NAMESPACE