void BanditSingleSparseStump::init() {
const int numClasses = _pTrainingData->getNumClasses();
const int numColumns = _pTrainingData->getNumAttributes();
const int armNumber = _banditAlgo->getArmNumber();
if ( numColumns < armNumber )
{
cerr << "The number of colums smaller than the number of the arms!!!!!!" << endl;
exit( -1 );
}
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("SingleSparseStumpLearner");
_banditAlgo->setArmNumber( numColumns );
vector<AlphaReal> initialValues( numColumns );
for( int i=0; i < numColumns; i++ )
{
SingleSparseStumpLearner* singleStump = dynamic_cast<SingleSparseStumpLearner*>( pWeakHypothesisSource->create());
singleStump->setTrainingData(_pTrainingData);
AlphaReal energy = singleStump->run( i );
AlphaReal edge = singleStump->getEdge();
AlphaReal reward = getRewardFromEdge( (AlphaReal) edge );
initialValues[i] = reward;
delete singleStump;
}
_banditAlgo->initialize( initialValues );
}
// ------------------------------------------------------------------------------
void BanditSingleStumpLearner::estimatePayoffs( vector<AlphaReal>& payoffs )
{
set<int> oldIndexSet;
set<int> randomIndexSet;
const int numExamples = _pTrainingData->getNumExamples();
const int numColumns = _pTrainingData->getNumAttributes();
_pTrainingData->getIndexSet( oldIndexSet );
int numSubset = static_cast<int>( static_cast<double>(numExamples) * _percentage );
if ( numSubset < 2 ) {
//use the whole dataset, do nothing
} else {
for (int j = 0; j < numExamples; ++j)
{
// Tricky way to select numOfDimensions columns randomly out of numColumns
int rest = numExamples - j;
AlphaReal r = rand()/static_cast<AlphaReal>(RAND_MAX);
if ( static_cast<AlphaReal>(numSubset) / rest > r )
{
--numSubset;
randomIndexSet.insert( j );
}
}
_pTrainingData->loadIndexSet( randomIndexSet );
}
payoffs.resize( numColumns );
BaseLearner* pWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("SingleStumpLearner");
for( int i=0; i < numColumns; i++ )
{
if ( payoffs[i] > 0.0 ) continue;
SingleStumpLearner* singleStump = dynamic_cast<SingleStumpLearner*>( pWeakHypothesisSource->create());
singleStump->setTrainingData(_pTrainingData);
AlphaReal energy = singleStump->run( i );
AlphaReal edge = singleStump->getEdge();
AlphaReal reward = getRewardFromEdge( (float) edge );
payoffs[i] = reward;
delete singleStump;
}
//restore the database
_pTrainingData->loadIndexSet( oldIndexSet );
}
//-------------------------------------------------------------------------------
float BanditTreeLearner::run()
{
if ( ! this->_banditAlgo->isInitialized() ) {
init();
}
// the bandit algorithm selects the subset the tree learner is aloowed to use
// the armindexes will be stored in _armsForPulling
getArms();
//declarations
NodePoint tmpNodePoint, nodeLeft, nodeRight;
ScalarLearner* pPreviousBaseLearner = 0;
set< int > tmpIdx;
//this contains the current number of baselearners
int ib = 0;
floatInnerNode innerNode;
// for storing the inner point (learneres) which will be extended
priority_queue<floatInnerNode, deque<floatInnerNode>, greater_first<floatInnerNode> > pq;
for(int ib = 0; ib < _numBaseLearners; ++ib)
_baseLearners[ib]->setTrainingData(_pTrainingData);
//train the first learner on the whole dataset
_pTrainingData->clearIndexSet();
pPreviousBaseLearner = dynamic_cast<ScalarLearner* >(_baseLearners[0]->copyState());
float energy = dynamic_cast< FeaturewiseLearner* >(pPreviousBaseLearner)->run( _armsForPulling );
if ( energy != energy )
{
if (_verbose > 2) {
cout << "Cannot find weak hypothesis, constant learner is used!!" << endl;
}
delete pPreviousBaseLearner;
//cannot find better than constant learner
ScalarLearner* pConstantWeakHypothesisSource =
dynamic_cast<ScalarLearner*>(BaseLearner::RegisteredLearners().getLearner("ConstantLearner"));
ScalarLearner* cLearner = dynamic_cast<ScalarLearner*>(pConstantWeakHypothesisSource->create());
cLearner->setTrainingData(_pTrainingData);
float constantEnergy = cLearner->run();
tmpNodePoint._edge = 0.0;
tmpNodePoint._learner = cLearner;
tmpNodePoint._idx = 0;
tmpNodePoint._learnerIdxSet = tmpIdx;
tmpNodePoint._extended = false;
} else {
//try to extend the root
for( int i = 0; i < _pTrainingData->getNumExamples(); i++ ) tmpIdx.insert( i );
tmpNodePoint._edge = pPreviousBaseLearner->getEdge();
tmpNodePoint._learner = pPreviousBaseLearner;
tmpNodePoint._idx = 0;
tmpNodePoint._learnerIdxSet = tmpIdx;
calculateChildrenAndEnergies( tmpNodePoint );
}
////////////////////////////////////////////////////////
//insert the root into the priority queue
if ( tmpNodePoint._extended )
{
if (_verbose > 2) {
//cout << "Edges: (parent, pos, neg): " << bLearnerVector[0].first << " " << bLearnerVector[1].first << " " << bLearnerVector[2].first << endl << flush;
//cout << "alpha[" << (ib) << "] = " << _alpha << endl << flush;
cout << "Edges: (parent, pos, neg): " << tmpNodePoint._edge << " " << tmpNodePoint._leftEdge << " " << tmpNodePoint._rightEdge << endl << flush;
}
// if the energy is getting higher then we push it into the priority queue
if ( tmpNodePoint._edge < ( tmpNodePoint._leftEdge + tmpNodePoint._rightEdge ) ) {
float deltaEdge = abs( tmpNodePoint._edge - ( tmpNodePoint._leftEdge + tmpNodePoint._rightEdge ) );
innerNode.first = deltaEdge;
innerNode.second = tmpNodePoint;
pq.push( innerNode );
} else {
//delete bLearnerVector[0].second.first.first;
delete tmpNodePoint._leftChild;
delete tmpNodePoint._rightChild;
}
}
// we cannot extend the root node
if ( pq.empty() ) {
// we don't extend the root
BaseLearner* tmpBL = _baseLearners[0];
_baseLearners[0] = tmpNodePoint._learner;
delete tmpBL;
ib = 1;
}
while ( ! pq.empty() && ( ib < _numBaseLearners ) ) {
//get the best learner from the priority queue
innerNode = pq.top();
if (_verbose > 2) {
cout << "Delta edge: " << innerNode.first << endl << flush;
cout << "Size of priority queue: " << pq.size() << endl << flush;
}
pq.pop();
tmpNodePoint = innerNode.second;
nodeLeft._edge = tmpNodePoint._leftEdge;
nodeLeft._learner = tmpNodePoint._leftChild;
nodeLeft._learnerIdxSet = tmpNodePoint._leftChildIdxSet;
nodeRight._edge = tmpNodePoint._rightEdge;
nodeRight._learner = tmpNodePoint._rightChild;
nodeRight._learnerIdxSet = tmpNodePoint._rightChildIdxSet;
//store the baselearner if the delta enrgy will be higher
if ( _verbose > 3 ) {
cout << "Insert learner: " << ib << endl << flush;
}
int parentIdx = tmpNodePoint._parentIdx;
BaseLearner* tmpBL = _baseLearners[ib];
_baseLearners[ib] = tmpNodePoint._learner;
delete tmpBL;
nodeLeft._parentIdx = ib;
nodeRight._parentIdx = ib;
nodeLeft._leftOrRightChild = 0;
nodeRight._leftOrRightChild = 1;
if ( ib > 0 ) {
//set the descendant idx
_idxPairs[ parentIdx ][ tmpNodePoint._leftOrRightChild ] = ib;
}
ib++;
if ( ib >= _numBaseLearners ) break;
//extend positive node
if ( nodeLeft._learner ) {
calculateChildrenAndEnergies( nodeLeft );
} else {
nodeLeft._extended = false;
}
//calculateChildrenAndEnergies( nodeLeft );
// if the energy is getting higher then we push it into the priority queue
if ( nodeLeft._extended )
{
if (_verbose > 2) {
//cout << "Edges: (parent, pos, neg): " << bLearnerVector[0].first << " " << bLearnerVector[1].first << " " << bLearnerVector[2].first << endl << flush;
//cout << "alpha[" << (ib) << "] = " << _alpha << endl << flush;
cout << "Edges: (parent, pos, neg): " << nodeLeft._edge << " " << nodeLeft._leftEdge << " " << nodeLeft._rightEdge << endl << flush;
}
// if the energy is getting higher then we push it into the priority queue
if ( nodeLeft._edge < ( nodeLeft._leftEdge + nodeLeft._rightEdge ) ) {
float deltaEdge = abs( nodeLeft._edge - ( nodeLeft._leftEdge + nodeLeft._rightEdge ) );
innerNode.first = deltaEdge;
innerNode.second = nodeLeft;
pq.push( innerNode );
} else {
//delete bLearnerVector[0].second.first.first;
delete nodeLeft._leftChild;
delete nodeLeft._rightChild;
if ( ib >= _numBaseLearners ) {
delete nodeLeft._learner;
break;
} else {
//this will be a leaf, we do not extend further
int parentIdx = nodeLeft._parentIdx;
BaseLearner* tmpBL = _baseLearners[ib];
_baseLearners[ib] = nodeLeft._learner;
delete tmpBL;
_idxPairs[ parentIdx ][ 0 ] = ib;
ib += 1;
}
}
}
//extend negative node
if ( nodeRight._learner ) {
calculateChildrenAndEnergies( nodeRight );
} else {
nodeRight._extended = false;
}
// if the energie is getting higher then we push it into the priority queue
if ( nodeRight._extended )
{
if (_verbose > 2) {
cout << "Edges: (parent, pos, neg): " << nodeRight._edge << " " << nodeRight._leftEdge << " " << nodeRight._rightEdge << endl << flush;
//cout << "alpha[" << (ib) << "] = " << _alpha << endl << flush;
}
// if the energie is getting higher then we push it into the priority queue
if ( nodeRight._edge < ( nodeRight._leftEdge + nodeRight._rightEdge ) ) {
float deltaEdge = abs( nodeRight._edge - ( nodeRight._leftEdge + nodeRight._rightEdge ) );
innerNode.first = deltaEdge;
innerNode.second = nodeRight;
pq.push( innerNode );
} else {
//delete bLearnerVector[0].second.first.first;
delete nodeRight._leftChild;
delete nodeRight._rightChild;
if ( ib >= _numBaseLearners ) {
delete nodeRight._learner;
break;
} else {
//this will be a leaf, we do not extend further
int parentIdx = nodeRight._parentIdx;
BaseLearner* tmpBL = _baseLearners[ib];
_baseLearners[ib] = nodeRight._learner;
delete tmpBL;
_idxPairs[ parentIdx ][ 1 ] = ib;
ib += 1;
}
}
}
}
for(int ib2 = ib; ib2 < _numBaseLearners; ++ib2) delete _baseLearners[ib2];
_numBaseLearners = ib;
if (_verbose > 2) {
cout << "Num of learners: " << _numBaseLearners << endl << flush;
}
//clear the priority queur
while ( ! pq.empty() && ( ib < _numBaseLearners ) ) {
//get the best learner from the priority queue
innerNode = pq.top();
pq.pop();
tmpNodePoint = innerNode.second;
delete tmpNodePoint._learner;
delete tmpNodePoint._leftChild;
delete tmpNodePoint._rightChild;
}
_id = _baseLearners[0]->getId();
for(int ib = 0; ib < _numBaseLearners; ++ib)
_id += "_x_" + _baseLearners[ib]->getId();
//calculate alpha
this->_alpha = 0.0;
float eps_min = 0.0, eps_pls = 0.0;
_pTrainingData->clearIndexSet();
for( int i = 0; i < _pTrainingData->getNumExamples(); i++ ) {
vector< Label> l = _pTrainingData->getLabels( i );
for( vector< Label >::iterator it = l.begin(); it != l.end(); it++ ) {
float result = this->classify( _pTrainingData, i, it->idx );
if ( ( result * it->y ) < 0 ) eps_min += it->weight;
if ( ( result * it->y ) > 0 ) eps_pls += it->weight;
}
}
// set the smoothing value to avoid numerical problem
// when theta=0.
setSmoothingVal( (float)(1.0 / _pTrainingData->getNumExamples() * 0.01 ) );
this->_alpha = getAlpha( eps_min, eps_pls );
// calculate the energy (sum of the energy of the leaves
energy = this->getEnergy( eps_min, eps_pls );
//bandit part we calculate the reward
_reward = getRewardFromEdge( getEdge() );
provideRewardForBanditAlgo();
return energy;
}
float BanditProductLearner::run()
{
if ( ! this->_banditAlgo->isInitialized() ) {
init();
}
// the bandit algorithm selects the subset the tree learner is allowed to use
// the armindexes will be stored in _armsForPulling
getArms();
const int numClasses = _pTrainingData->getNumClasses();
const int numExamples = _pTrainingData->getNumExamples();
// Backup original labels
for (int i = 0; i < numExamples; ++i) {
const vector<Label>& labels = _pTrainingData->getLabels(i);
vector<char> exampleLabels;
for (int l = 0; l < numClasses; ++l)
exampleLabels.push_back(labels[l].y);
_savedLabels.push_back(exampleLabels);
}
for(int ib = 0; ib < _numBaseLearners; ++ib)
_baseLearners[ib]->setTrainingData(_pTrainingData);
float energy = numeric_limits<float>::max();
float previousEnergy, hx, previousAlpha;
BaseLearner* pPreviousBaseLearner = 0;
bool firstLoop = true;
int ib = -1;
while (1) {
ib += 1;
if (ib >= _numBaseLearners) {
ib = 0;
firstLoop = false;
}
previousEnergy = energy;
previousAlpha = _alpha;
if (pPreviousBaseLearner)
delete pPreviousBaseLearner;
if ( !firstLoop ) {
// take the old learner off the labels
for (int i = 0; i < numExamples; ++i) {
vector<Label>& labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l) {
// Here we could have the option of using confidence rated setting so the
// real valued output of classify instead of its sign
hx = _baseLearners[ib]->classify(_pTrainingData,i,l);
if ( hx < 0 )
labels[l].y *= -1;
else if ( hx == 0 ) { // have to redo the multiplications, haven't been tested
for(int ib1 = 0; ib1 < _numBaseLearners && labels[l].y != 0; ++ib1) {
if (ib != ib1) {
hx = _baseLearners[ib1]->classify(_pTrainingData,i,l);
if (hx < 0)
labels[l].y *= -1;
else if (hx == 0)
labels[l].y = 0;
}
}
}
}
}
}
pPreviousBaseLearner = _baseLearners[ib]->copyState();
energy = dynamic_cast< FeaturewiseLearner* >(_baseLearners[ib])->run(_armsForPulling );
// check if it is signailing_nan
if ( energy != energy )
{
if (_verbose > 2) {
cout << "Cannot find weak hypothesis, constant learner is used!!" << endl;
}
BaseLearner* pConstantWeakHypothesisSource =
BaseLearner::RegisteredLearners().getLearner("ConstantLearner");
BaseLearner* pConstantWeakHypothesis = pConstantWeakHypothesisSource->create() ;
pConstantWeakHypothesis->setTrainingData( _pTrainingData );
energy = pConstantWeakHypothesis->run();
delete _baseLearners[ib];
_baseLearners[ib] = pConstantWeakHypothesis;
}
_alpha = _baseLearners[ib]->getAlpha();
if (_verbose > 2) {
cout << "E[" << (ib+1) << "] = " << energy << endl << flush;
cout << "alpha[" << (ib+1) << "] = " << _alpha << endl << flush;
}
for (int i = 0; i < numExamples; ++i) {
vector<Label>& labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l) {
// Here we could have the option of using confidence rated setting so the
// real valued output of classify instead of its sign
if (labels[l].y != 0) { // perhaps replace it by nor_utils::is_zero(labels[l].y)
hx = _baseLearners[ib]->classify(_pTrainingData,i,l);
if ( hx < 0 )
labels[l].y *= -1;
else if ( hx == 0 )
labels[l].y = 0;
}
}
}
// We have to do at least one full iteration. For real it's not guaranteed
// Alternatively we could initialize all of them to constant
// if ( !firstLoop && energy >= previousEnergy ) {
// if (energy > previousEnergy) {
// _baseLearners[ib] = pPreviousBaseLearner->copyState();
// delete pPreviousBaseLearner;
// energy = previousEnergy;
// _alpha = _baseLearners[ib]->getAlpha();
// }
// break;
// }
if ( energy >= previousEnergy ) {
_alpha = previousAlpha;
energy = previousEnergy;
if (firstLoop) {
for(int ib2 = ib; ib2 < _numBaseLearners; ++ib2)
delete _baseLearners[ib2];
_numBaseLearners = ib;
}
else {
_baseLearners[ib] = pPreviousBaseLearner->copyState();
}
delete pPreviousBaseLearner;
break;
}
}
// Restore original labels
for (int i = 0; i < numExamples; ++i) {
vector<Label>& labels = _pTrainingData->getLabels(i);
for (int l = 0; l < numClasses; ++l)
labels[l].y = _savedLabels[i][l];
}
_id = _baseLearners[0]->getId();
for(int ib = 1; ib < _numBaseLearners; ++ib)
_id += "_x_" + _baseLearners[ib]->getId();
//bandit part we calculate the reward
_reward = getRewardFromEdge( getEdge() );
provideRewardForBanditAlgo();
return energy;
}
