bool RandomForests::train_(ClassificationData &trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data has zero samples!" << endl;
return false;
}
if( bootstrappedDatasetWeight <= 0.0 || bootstrappedDatasetWeight > 1.0 ){
errorLog << "train_(ClassificationData &trainingData) - Bootstrapped Dataset Weight must be [> 0.0 and <= 1.0]" << endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Flag that the main algorithm has been trained encase we need to trigger any callbacks
trained = true;
//Train the random forest
forest.reserve( forestSize );
for(UINT i=0; i<forestSize; i++){
//Get a balanced bootstrapped dataset
UINT datasetSize = (UINT)(trainingData.getNumSamples() * bootstrappedDatasetWeight);
ClassificationData data = trainingData.getBootstrappedDataset( datasetSize, true );
DecisionTree tree;
tree.setDecisionTreeNode( *decisionTreeNode );
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setTrainingMode( trainingMode );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
tree.enableNullRejection( useNullRejection );
tree.setRemoveFeaturesAtEachSpilt( removeFeaturesAtEachSpilt );
trainingLog << "Training forest " << i+1 << "/" << forestSize << "..." << endl;
//Train this tree
if( !tree.train( data ) ){
errorLog << "train_(ClassificationData &labelledTrainingData) - Failed to train tree at forest index: " << i << endl;
clear();
return false;
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
return true;
}
bool RandomForests::train_(ClassificationData &trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train_(ClassificationData &trainingData) - Training data has zero samples!" << std::endl;
return false;
}
if( bootstrappedDatasetWeight <= 0.0 || bootstrappedDatasetWeight > 1.0 ){
errorLog << "train_(ClassificationData &trainingData) - Bootstrapped Dataset Weight must be [> 0.0 and <= 1.0]" << std::endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
if( useValidationSet ){
validationSetAccuracy = 0;
validationSetPrecision.resize( useNullRejection ? K+1 : K, 0 );
validationSetRecall.resize( useNullRejection ? K+1 : K, 0 );
}
//Flag that the main algorithm has been trained encase we need to trigger any callbacks
trained = true;
//Train the random forest
forest.reserve( forestSize );
for(UINT i=0; i<forestSize; i++){
//Get a balanced bootstrapped dataset
UINT datasetSize = (UINT)(trainingData.getNumSamples() * bootstrappedDatasetWeight);
ClassificationData data = trainingData.getBootstrappedDataset( datasetSize, true );
Timer timer;
timer.start();
DecisionTree tree;
tree.setDecisionTreeNode( *decisionTreeNode );
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setUseValidationSet( useValidationSet );
tree.setValidationSetSize( validationSetSize );
tree.setTrainingMode( trainingMode );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
tree.enableNullRejection( useNullRejection );
tree.setRemoveFeaturesAtEachSpilt( removeFeaturesAtEachSpilt );
trainingLog << "Training decision tree " << i+1 << "/" << forestSize << "..." << std::endl;
//Train this tree
if( !tree.train_( data ) ){
errorLog << "train_(ClassificationData &trainingData) - Failed to train tree at forest index: " << i << std::endl;
clear();
return false;
}
Float computeTime = timer.getMilliSeconds();
trainingLog << "Decision tree trained in " << (computeTime*0.001)/60.0 << " minutes" << std::endl;
if( useValidationSet ){
Float forestNorm = 1.0 / forestSize;
validationSetAccuracy += tree.getValidationSetAccuracy();
VectorFloat precision = tree.getValidationSetPrecision();
VectorFloat recall = tree.getValidationSetRecall();
grt_assert( precision.getSize() == validationSetPrecision.getSize() );
grt_assert( recall.getSize() == validationSetRecall.getSize() );
for(UINT i=0; i<validationSetPrecision.getSize(); i++){
validationSetPrecision[i] += precision[i] * forestNorm;
}
for(UINT i=0; i<validationSetRecall.getSize(); i++){
validationSetRecall[i] += recall[i] * forestNorm;
}
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
if( useValidationSet ){
validationSetAccuracy /= forestSize;
trainingLog << "Validation set accuracy: " << validationSetAccuracy << std::endl;
trainingLog << "Validation set precision: ";
for(UINT i=0; i<validationSetPrecision.getSize(); i++){
trainingLog << validationSetPrecision[i] << " ";
}
trainingLog << std::endl;
trainingLog << "Validation set recall: ";
for(UINT i=0; i<validationSetRecall.getSize(); i++){
trainingLog << validationSetRecall[i] << " ";
}
trainingLog << std::endl;
}
return true;
}