MultidimensionalRegression.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.
*/

#define GRT_DLL_EXPORTS
#include "MultidimensionalRegression.h"

GRT_BEGIN_NAMESPACE

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

MultidimensionalRegression::MultidimensionalRegression(const Regressifier &regressifier,bool useScaling):regressifier(NULL)
{
 this->useScaling = useScaling;
 classType = "MultidimensionalRegression";
 regressifierType = classType;
 debugLog.setProceedingText("[DEBUG MultidimensionalRegression]");
 errorLog.setProceedingText("[ERROR MultidimensionalRegression]");
 trainingLog.setProceedingText("[TRAINING MultidimensionalRegression]");
 warningLog.setProceedingText("[WARNING MultidimensionalRegression]");
 
 setRegressionModule( regressifier );
}

MultidimensionalRegression::~MultidimensionalRegression(void)
{
 deleteAll();
}

MultidimensionalRegression& MultidimensionalRegression::operator=(const MultidimensionalRegression &rhs){
 
 if( this != &rhs ){
 
 //Clean up any previous setup
 deleteAll();
 
 //Set the main regression module
 setRegressionModule( *rhs.getRegressifier() );
 
 //Deep copy the regression modules from the RHS to this instance
 if( !rhs.deepCopyRegressionModules( regressionModules ) ){
 errorLog << "const MultidimensionalRegression &rhs - Failed to deep copy regression modules!" << std::endl;
 }
 
 //Copy the base variables
 copyBaseVariables( (Regressifier*)&rhs );
 }
 return *this;
}

bool MultidimensionalRegression::deepCopyFrom(const Regressifier *regressifier){
 
 if( regressifier == NULL ) return false;
 
 if( this->getRegressifierType() == regressifier->getRegressifierType() ){
 
 const MultidimensionalRegression *ptr = dynamic_cast<const MultidimensionalRegression*>(regressifier);
 
 //Set the main regression module
 setRegressionModule( *ptr->getRegressifier() );
 
 //Deep copy the regression modules from the RHS to this instance
 if( !ptr->deepCopyRegressionModules( regressionModules ) ){
 errorLog << "deepCopyFrom(const Regressifier *regressifier) - Failed to deep copy regression modules!" << std::endl;
 return false;
 }
 
 //Copy the base variables
 return copyBaseVariables( regressifier );
 }
 
 return false;
}

bool MultidimensionalRegression::train_(RegressionData &trainingData){
 
 const unsigned int M = trainingData.getNumSamples();
 const unsigned int N = trainingData.getNumInputDimensions();
 const unsigned int K = trainingData.getNumTargetDimensions();
 trained = false;
 trainingResults.clear();
 deleteRegressionModules();
 
 if( !getIsRegressionModuleSet() ){
 errorLog << "train_(RegressionData &trainingData) - The regression module has not been set!" << std::endl;
 return false;
 }
 
 if( M == 0 ){
 errorLog << "train_(RegressionData &trainingData) - Training data has zero samples!" << std::endl;
 return false;
 }
 
 numInputDimensions = N;
 numOutputDimensions = K;
 inputVectorRanges.clear();
 targetVectorRanges.clear();
 
 //Scale the training and validation data, if needed
 if( useScaling ){
 //Find the ranges for the input data
 inputVectorRanges = trainingData.getInputRanges();
 
 //Find the ranges for the target data
 targetVectorRanges = trainingData.getTargetRanges();
 
 //Scale the training data
 trainingData.scale(inputVectorRanges,targetVectorRanges,0.0,1.0);
 }
 
 //Setup the regression modules
 regressionModules.resize( K, NULL );
 
 //Any scaling will happpen at the meta level, not the regression module letter, so ensure scaling is turned off for the modules
 regressifier->enableScaling( false );
 
 for(UINT k=0; k<K; k++){
 regressionModules[k] = regressifier->deepCopy();
 if( regressionModules[k] == NULL ){
 errorLog << "train(LabelledRegressionData &trainingData) - Failed to deep copy module " << k << std::endl;
 return false;
 }
 }
 
 //Train each regression module
 for(UINT k=0; k<K; k++){
 
 trainingLog << "Training regression module: " << k << std::endl;
 
 //We need to create a 1 dimensional training dataset for the k'th target dimension
 RegressionData data;
 data.setInputAndTargetDimensions(N, 1);
 
 for(UINT i=0; i<M; i++){
 if( !data.addSample(trainingData[i].getInputVector(), VectorFloat(1,trainingData[i].getTargetVector()[k]) ) ){
 errorLog << "train_(RegressionData &trainingData) - Failed to add sample to dataset for regression module " << k << std::endl;
 return false;
 }
 }
 
 if( !regressionModules[k]->train( data ) ){
 errorLog << "train_(RegressionData &trainingData) - Failed to train regression module " << k << std::endl;
 return false;
 }
 }
 
 //Flag that the algorithm has been trained
 regressionData.resize(K,0);
 trained = true;
 return trained;
}

bool MultidimensionalRegression::predict_(VectorFloat &inputVector){
 
 if( !trained ){
 errorLog << "predict_(VectorFloat &inputVector) - Model Not Trained!" << std::endl;
 return false;
 }
 
 if( !trained ) return false;
 
 if( inputVector.getSize() != numInputDimensions ){
 errorLog << "predict_(VectorFloat &inputVector) - The size of the input Vector (" << inputVector.getSize() << ") 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] = grt_scale(inputVector[n], inputVectorRanges[n].minValue, inputVectorRanges[n].maxValue, 0.0, 1.0);
 }
 }
 
 for(UINT n=0; n<numOutputDimensions; n++){
 if( !regressionModules[ n ]->predict( inputVector ) ){
 errorLog << "predict_(VectorFloat &inputVector) - Failed to predict for regression module " << n << std::endl;
 }
 regressionData[ n ] = regressionModules[ n ]->getRegressionData()[0];
 }
 
 if( useScaling ){
 for(UINT n=0; n<numOutputDimensions; n++){
 regressionData[n] = grt_scale(regressionData[n], 0.0, 1.0, targetVectorRanges[n].minValue, targetVectorRanges[n].maxValue);
 }
 }
 
 return true;
}

bool MultidimensionalRegression::save( std::fstream &file ) const {
 
 if(!file.is_open())
 {
 errorLog << "save(fstream &file) - The file is not open!" << std::endl;
 return false;
 }
 
 //Write the header info
 file << "GRT_MULTIDIMENSIONAL_REGRESSION_MODEL_FILE_V2.0\n";
 
 //Write the regressifier settings to the file
 if( !Regressifier::saveBaseSettingsToFile(file) ){
 errorLog <<"save(fstream &file) - Failed to save Regressifier base settings to file!" << std::endl;
 return false;
 }
 
 if( !getIsRegressionModuleSet() ){
 file << "Regressifier: " << "NOT_SET" << std::endl;
 return true;
 }
 
 //Save the regression
 file << "Regressifier: " << regressifier->getRegressifierType() << std::endl;
 
 if( !regressifier->save( file ) ){
 errorLog << "save(fstream &file) - Failed to save regressifier!" << std::endl;
 return false;
 }
 
 for(UINT i=0; i<regressionModules.size(); i++){
 if( !regressionModules[i]->save( file ) ){
 errorLog << "save(fstream &file) - Failed to save regression module " << i << std::endl;
 return false;
 }
 }
 
 return true;
}

bool MultidimensionalRegression::load( std::fstream &file ){
 
 trained = false;
 numInputDimensions = 0;
 deleteAll();
 
 if(!file.is_open())
 {
 errorLog << "load(string filename) - Could not open file to load model" << std::endl;
 return false;
 }
 
 std::string word;
 
 //Find the file type header
 file >> word;
 
 //Check to see if we should load a legacy file
 if( word == "GRT_MULTIDIMENSIONAL_REGRESSION_MODEL_FILE_V1.0" ){
 return loadLegacyModelFromFile( file );
 }
 
 if( word != "GRT_MULTIDIMENSIONAL_REGRESSION_MODEL_FILE_V2.0" ){
 errorLog << "load( fstream &file ) - Could not find Model File Header" << std::endl;
 return false;
 }
 
 //Load the regressifier settings from the file
 if( !Regressifier::loadBaseSettingsFromFile(file) ){
 errorLog <<"load( fstream &file ) - Failed to save Regressifier base settings to file!" << std::endl;
 return false;
 }
 
 file >> word;
 if( word != "Regressifier:" ){
 errorLog << "load(string filename) - Failed to find Regressifier!" << std::endl;
 return false;
 }
 
 //Load the regressifier type
 std::string regressifierType;
 file >> regressifierType;
 if( regressifierType == "NOT_SET" ){
 return true;
 }
 
 //Create the regressifer
 regressifier = createInstanceFromString( regressifierType );
 
 if( regressifier == NULL ){
 errorLog << "load(fstream &file) - Failed to create regression instance from string!" << std::endl;
 return false;
 }
 
 if( !regressifier->load( file ) ){
 errorLog <<"load(fstream &file) - Failed to load regressifier!" << std::endl;
 return false;
 }
 
 if( numOutputDimensions > 0 ){
 //Initialize the regression modules
 regressionModules.resize(numOutputDimensions, NULL);
 
 for(UINT i=0; i<regressionModules.getSize(); i++){
 regressionModules[i] = createInstanceFromString( regressifierType );
 if( !regressionModules[i]->load( file ) ){
 errorLog << "load(fstream &file) - Failed to load regression module " << i << std::endl;
 return false;
 }
 }
 }
 
 return true;
}

bool MultidimensionalRegression::getIsRegressionModuleSet() const {
 return regressifier != NULL ? true : false;
}

Regressifier* MultidimensionalRegression::getRegressifier() const {
 return regressifier;
}

bool MultidimensionalRegression::setRegressionModule( const Regressifier &regressifier ){
 
 if( !deleteRegressionModules() ){
 return false;
 }
 
 trained = false;
 
 if( this->regressifier != NULL ) delete this->regressifier;
 
 this->regressifier = regressifier.deepCopy();
 
 if( this->regressifier == NULL ) return false;
 
 return true;
}

bool MultidimensionalRegression::deepCopyRegressionModules( Vector< Regressifier* > &newModules ) const{
 
 const UINT N = regressionModules.getSize();
 
 //The newModules Vector should be empty
 if( newModules.size() > 0 ) return false;
 
 //Return true if there are no modules to copy
 if( N == 0 ) return true;
 
 //Perform the copy
 newModules.resize( N );
 
 for(UINT i=0; i<N; i++){
 //Deep copy the i'th module into the i'th regressionModules
 newModules[i] = regressionModules[i]->deepCopy();
 if( newModules[i] == NULL ){
 for(UINT j=0; j<i; j++){
 delete newModules[j];
 newModules[j] = NULL;
 }
 newModules.clear();
 return false;
 }
 }
 
 return true;
}

bool MultidimensionalRegression::deleteAll(){
 if( regressifier != NULL ){
 delete regressifier;
 regressifier = NULL;
 }
 return deleteRegressionModules();
}

bool MultidimensionalRegression::deleteRegressionModules(){
 
 const UINT N = regressionModules.getSize();
 
 if( N == 0 ) return true;
 
 for(UINT i=0; i<N; i++){
 delete regressionModules[i];
 regressionModules[i] = NULL;
 }
 regressionModules.clear();
 return true;
}

bool MultidimensionalRegression::loadLegacyModelFromFile( std::fstream &file ){
 
 std::string word;
 
 file >> word;
 if(word != "NumFeatures:"){
 errorLog << "load(string filename) - Could not find NumFeatures!" << std::endl;
 return false;
 }
 file >> numInputDimensions;
 
 file >> word;
 if(word != "NumOutputDimensions:"){
 errorLog << "load(string filename) - Could not find NumOutputDimensions!" << std::endl;
 return false;
 }
 file >> numOutputDimensions;
 
 file >> word;
 if(word != "UseScaling:"){
 errorLog << "load(string filename) - Could not find UseScaling!" << std::endl;
 return false;
 }
 file >> useScaling;
 
 if( useScaling ){
 //Resize the ranges buffer
 inputVectorRanges.resize(numInputDimensions);
 targetVectorRanges.resize(numOutputDimensions);
 
 //Load the ranges
 file >> word;
 if(word != "InputVectorRanges:"){
 file.close();
 errorLog << "load(string filename) - Failed to find InputVectorRanges!" << std::endl;
 return false;
 }
 for(UINT j=0; j<inputVectorRanges.getSize(); j++){
 file >> inputVectorRanges[j].minValue;
 file >> inputVectorRanges[j].maxValue;
 }
 
 file >> word;
 if(word != "OutputVectorRanges:"){
 file.close();
 errorLog << "load(string filename) - Failed to find OutputVectorRanges!" << std::endl;
 return false;
 }
 for(UINT j=0; j<targetVectorRanges.getSize(); j++){
 file >> targetVectorRanges[j].minValue;
 file >> targetVectorRanges[j].maxValue;
 }
 }
 
 file >> word;
 if( word != "Regressifier:" ){
 errorLog << "load(string filename) - Failed to find Regressifier!" << std::endl;
 return false;
 }
 
 //Load the regressifier type
 std::string regressifierType;
 file >> regressifierType;
 if( regressifierType == "NOT_SET" ){
 return true;
 }
 
 //Create the regressifer
 regressifier = createInstanceFromString( regressifierType );
 
 if( regressifier == NULL ){
 errorLog << "load(fstream &file) - Failed to create regression instance from string!" << std::endl;
 return false;
 }
 
 if( !regressifier->load( file ) ){
 errorLog <<"load(fstream &file) - Failed to load regressifier!" << std::endl;
 return false;
 }
 
 if( numOutputDimensions > 0 ){
 //Initialize the regression modules
 regressionModules.resize(numOutputDimensions, NULL);
 
 for(UINT i=0; i<regressionModules.getSize(); i++){
 regressionModules[i] = createInstanceFromString( regressifierType );
 if( !regressionModules[i]->load( file ) ){
 errorLog << "load(fstream &file) - Failed to load regression module " << i << std::endl;
 return false;
 }
 }
 
 //Resize the regression data Vector
 regressionData.resize(numOutputDimensions,0);
 
 //Flag that the model has been trained
 trained = true;
 }
 
 return true;
}

GRT_END_NAMESPACE