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// C++ Interface: rf_ridge_split
//
// Description:
//
//
// Author: Nico Splitthoff <splitthoff@zg00103>, (C) 2009
//
// Copyright: See COPYING file that comes with this distribution
//
//
#ifndef VIGRA_RANDOM_FOREST_RIDGE_SPLIT_H
#define VIGRA_RANDOM_FOREST_RIDGE_SPLIT_H
//#include "rf_sampling.hxx"
#include "../sampling.hxx"
#include "rf_split.hxx"
#include "rf_nodeproxy.hxx"
#include "../regression.hxx"
#define outm(v) std::cout << (#v) << ": " << (v) << std::endl;
#define outm2(v) std::cout << (#v) << ": " << (v) << ", ";
namespace vigra
{
/*template<>
class Node<i_RegrNode>
: public NodeBase
{
public:
typedef NodeBase BT;
Node( BT::T_Container_type & topology,
BT::P_Container_type & param,
int nNumCols)
: BT(5+nNumCols,2+nNumCols,topology, param)
{
BT::typeID() = i_RegrNode;
}
Node( BT::T_Container_type & topology,
BT::P_Container_type & param,
INT n )
: BT(5,2,topology, param, n)
{}
Node( BT & node_)
: BT(5, 2, node_)
{}
double& threshold()
{
return BT::parameters_begin()[1];
}
BT::INT& column()
{
return BT::column_data()[0];
}
template<class U, class C>
BT::INT& next(MultiArrayView<2,U,C> const & feature)
{
return (feature(0, column()) < threshold())? child(0):child(1);
}
};*/
template<class ColumnDecisionFunctor, class Tag = ClassificationTag>
class RidgeSplit: public SplitBase<Tag>
{
public:
typedef SplitBase<Tag> SB;
ArrayVector<Int32> splitColumns;
ColumnDecisionFunctor bgfunc;
double region_gini_;
ArrayVector<double> min_gini_;
ArrayVector<std::ptrdiff_t> min_indices_;
ArrayVector<double> min_thresholds_;
int bestSplitIndex;
//dns
bool m_bDoScalingInTraining;
bool m_bDoBestLambdaBasedOnGini;
RidgeSplit()
:m_bDoScalingInTraining(true),
m_bDoBestLambdaBasedOnGini(true)
{
}
double minGini() const
{
return min_gini_[bestSplitIndex];
}
int bestSplitColumn() const
{
return splitColumns[bestSplitIndex];
}
bool& doScalingInTraining()
{ return m_bDoScalingInTraining; }
bool& doBestLambdaBasedOnGini()
{ return m_bDoBestLambdaBasedOnGini; }
template<class T>
void set_external_parameters(ProblemSpec<T> const & in)
{
SB::set_external_parameters(in);
bgfunc.set_external_parameters(in);
int featureCount_ = in.column_count_;
splitColumns.resize(featureCount_);
for(int k=0; k<featureCount_; ++k)
splitColumns[k] = k;
min_gini_.resize(featureCount_);
min_indices_.resize(featureCount_);
min_thresholds_.resize(featureCount_);
}
template<class T, class C, class T2, class C2, class Region, class Random>
int findBestSplit(MultiArrayView<2, T, C> features,
MultiArrayView<2, T2, C2> multiClassLabels,
Region & region,
ArrayVector<Region>& childRegions,
Random & randint)
{
//std::cerr << "Split called" << std::endl;
typedef typename Region::IndexIterator IndexIterator;
typedef typename MultiArrayView <2, T, C>::difference_type fShape;
typedef typename MultiArrayView <2, T2, C2>::difference_type lShape;
typedef typename MultiArrayView <2, double>::difference_type dShape;
// calculate things that haven't been calculated yet.
// std::cout << "start" << std::endl;
if(std::accumulate(region.classCounts().begin(),
region.classCounts().end(), 0) != region.size())
{
RandomForestClassCounter< MultiArrayView<2,T2, C2>,
ArrayVector<double> >
counter(multiClassLabels, region.classCounts());
std::for_each( region.begin(), region.end(), counter);
region.classCountsIsValid = true;
}
// Is the region pure already?
region_gini_ = GiniCriterion::impurity(region.classCounts(),
region.size());
if(region_gini_ == 0 || region.size() < SB::ext_param_.actual_mtry_ || region.oob_size() < 2)
return SB::makeTerminalNode(features, multiClassLabels, region, randint);
// select columns to be tried.
for(int ii = 0; ii < SB::ext_param_.actual_mtry_; ++ii)
std::swap(splitColumns[ii],
splitColumns[ii+ randint(features.shape(1) - ii)]);
//do implicit binary case
MultiArray<2, T2> labels(lShape(multiClassLabels.shape(0),1));
//number of classes should be >1, otherwise makeTerminalNode would have been called
int nNumClasses=0;
for(int n=0; n<(int)region.classCounts().size(); n++)
nNumClasses+=((region.classCounts()[n]>0) ? 1:0);
//convert to binary case
if(nNumClasses>2)
{
int nMaxClass=0;
int nMaxClassCounts=0;
for(int n=0; n<(int)region.classCounts().size(); n++)
{
//this should occur in any case:
//we had more than two non-zero classes in order to get here
if(region.classCounts()[n]>nMaxClassCounts)
{
nMaxClassCounts=region.classCounts()[n];
nMaxClass=n;
}
}
//create binary labels
for(int n=0; n<multiClassLabels.shape(0); n++)
labels(n,0)=((multiClassLabels(n,0)==nMaxClass) ? 1:0);
}
else
labels=multiClassLabels;
//_do implicit binary case
//uncomment this for some debugging
/* int nNumCases=features.shape(0);
typedef typename MultiArrayView <2, int>::difference_type nShape;
MultiArray<2, int> elementCounterArray(nShape(nNumCases,1),(int)0);
int nUniqueElements=0;
for(int n=0; n<region.size(); n++)
elementCounterArray[region[n]]++;
for(int n=0; n<nNumCases; n++)
nUniqueElements+=((elementCounterArray[n]>0) ? 1:0);
outm(nUniqueElements);
nUniqueElements=0;
MultiArray<2, int> elementCounterArray_oob(nShape(nNumCases,1),(int)0);
for(int n=0; n<region.oob_size(); n++)
elementCounterArray_oob[region.oob_begin()[n]]++;
for(int n=0; n<nNumCases; n++)
nUniqueElements+=((elementCounterArray_oob[n]>0) ? 1:0);
outm(nUniqueElements);
int notUniqueElements=0;
for(int n=0; n<nNumCases; n++)
notUniqueElements+=(((elementCounterArray_oob[n]>0) && (elementCounterArray[n]>0)) ? 1:0);
outm(notUniqueElements);*/
//outm(SB::ext_param_.actual_mtry_);
//select submatrix of features for regression calculation
MultiArrayView<2, T, C> cVector;
MultiArray<2, T> xtrain(fShape(region.size(),SB::ext_param_.actual_mtry_));
//we only want -1 and 1 for this
MultiArray<2, double> regrLabels(dShape(region.size(),1));
//copy data into a vigra data structure and centre and scale while doing so
MultiArray<2, double> meanMatrix(dShape(SB::ext_param_.actual_mtry_,1));
MultiArray<2, double> stdMatrix(dShape(SB::ext_param_.actual_mtry_,1));
for(int m=0; m<SB::ext_param_.actual_mtry_; m++)
{
cVector=columnVector(features, splitColumns[m]);
//centre and scale the data
double dCurrFeatureColumnMean=0.0;
double dCurrFeatureColumnStd=1.0; //default value
//calc mean on bootstrap data
for(int n=0; n<region.size(); n++)
dCurrFeatureColumnMean+=cVector[region[n]];
dCurrFeatureColumnMean/=region.size();
//calc scaling
if(m_bDoScalingInTraining)
{
for(int n=0; n<region.size(); n++)
{
dCurrFeatureColumnStd+=
(cVector[region[n]]-dCurrFeatureColumnMean)*(cVector[region[n]]-dCurrFeatureColumnMean);
}
//unbiased std estimator:
dCurrFeatureColumnStd=sqrt(dCurrFeatureColumnStd/(region.size()-1));
}
//dCurrFeatureColumnStd is still 1.0 if we didn't want scaling
stdMatrix(m,0)=dCurrFeatureColumnStd;
meanMatrix(m,0)=dCurrFeatureColumnMean;
//get feature matrix, i.e. A (note that weighting is done automatically
//since rows can occur multiple times -> bagging)
for(int n=0; n<region.size(); n++)
xtrain(n,m)=(cVector[region[n]]-dCurrFeatureColumnMean)/dCurrFeatureColumnStd;
}
// std::cout << "middle" << std::endl;
//get label vector (i.e. b)
for(int n=0; n<region.size(); n++)
{
//we checked for/built binary case further up.
//class labels should thus be either 0 or 1
//-> convert to -1 and 1 for regression
regrLabels(n,0)=((labels[region[n]]==0) ? -1:1);
}
MultiArray<2, double> dLambdas(dShape(11,1));
int nCounter=0;
for(int nLambda=-5; nLambda<=5; nLambda++)
dLambdas[nCounter++]=pow(10.0,nLambda);
//destination vector for regression coefficients; use same type as for xtrain
MultiArray<2, double> regrCoef(dShape(SB::ext_param_.actual_mtry_,11));
ridgeRegressionSeries(xtrain,regrLabels,regrCoef,dLambdas);
double dMaxRidgeSum=NumericTraits<double>::min();
double dCurrRidgeSum;
int nMaxRidgeSumAtLambdaInd=0;
for(int nLambdaInd=0; nLambdaInd<11; nLambdaInd++)
{
//just sum up the correct answers
//(correct means >=intercept for class 1, <intercept for class 0)
//(intercept=0 or intercept=threshold based on gini)
dCurrRidgeSum=0.0;
//assemble projection vector
MultiArray<2, double> dDistanceFromHyperplane(dShape(features.shape(0),1));
for(int n=0; n<region.oob_size(); n++)
{
dDistanceFromHyperplane(region.oob_begin()[n],0)=0.0;
for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
{
dDistanceFromHyperplane(region.oob_begin()[n],0)+=
features(region.oob_begin()[n],splitColumns[m])*regrCoef(m,nLambdaInd);
}
}
double dCurrIntercept=0.0;
if(m_bDoBestLambdaBasedOnGini)
{
//calculate gini index
bgfunc(dDistanceFromHyperplane,
labels,
region.oob_begin(), region.oob_end(),
region.classCounts());
dCurrIntercept=bgfunc.min_threshold_;
}
else
{
for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
dCurrIntercept+=meanMatrix(m,0)*regrCoef(m,nLambdaInd);
}
for(int n=0; n<region.oob_size(); n++)
{
//check what lambda performs best on oob data
int nClassPrediction=((dDistanceFromHyperplane(region.oob_begin()[n],0) >=dCurrIntercept) ? 1:0);
dCurrRidgeSum+=((nClassPrediction == labels(region.oob_begin()[n],0)) ? 1:0);
}
if(dCurrRidgeSum>dMaxRidgeSum)
{
dMaxRidgeSum=dCurrRidgeSum;
nMaxRidgeSumAtLambdaInd=nLambdaInd;
}
}
// std::cout << "middle2" << std::endl;
//create a Node for output
Node<i_HyperplaneNode> node(SB::ext_param_.actual_mtry_, SB::t_data, SB::p_data);
//normalise coeffs
//data was scaled (by 1.0 or by std) -> take into account
MultiArray<2, double> dCoeffVector(dShape(SB::ext_param_.actual_mtry_,1));
for(int n=0; n<SB::ext_param_.actual_mtry_; n++)
dCoeffVector(n,0)=regrCoef(n,nMaxRidgeSumAtLambdaInd)*stdMatrix(n,0);
//calc norm
double dVnorm=columnVector(regrCoef,nMaxRidgeSumAtLambdaInd).norm();
for(int n=0; n<SB::ext_param_.actual_mtry_; n++)
node.weights()[n]=dCoeffVector(n,0)/dVnorm;
//_normalise coeffs
//save the columns
node.column_data()[0]=SB::ext_param_.actual_mtry_;
for(int n=0; n<SB::ext_param_.actual_mtry_; n++)
node.column_data()[n+1]=splitColumns[n];
//assemble projection vector
//careful here: "region" is a pointer to indices...
//all the indices in "region" need to have valid data
//convert from "region" space to original "feature" space
MultiArray<2, double> dDistanceFromHyperplane(dShape(features.shape(0),1));
for(int n=0; n<region.size(); n++)
{
dDistanceFromHyperplane(region[n],0)=0.0;
for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
{
dDistanceFromHyperplane(region[n],0)+=
features(region[n],m)*node.weights()[m];
}
}
for(int n=0; n<region.oob_size(); n++)
{
dDistanceFromHyperplane(region.oob_begin()[n],0)=0.0;
for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
{
dDistanceFromHyperplane(region.oob_begin()[n],0)+=
features(region.oob_begin()[n],m)*node.weights()[m];
}
}
//calculate gini index
bgfunc(dDistanceFromHyperplane,
labels,
region.begin(), region.end(),
region.classCounts());
// did not find any suitable split
if(closeAtTolerance(bgfunc.min_gini_, NumericTraits<double>::max()))
return SB::makeTerminalNode(features, multiClassLabels, region, randint);
//take gini threshold here due to scaling, normalisation, etc. of the coefficients
node.intercept() = bgfunc.min_threshold_;
SB::node_ = node;
childRegions[0].classCounts() = bgfunc.bestCurrentCounts[0];
childRegions[1].classCounts() = bgfunc.bestCurrentCounts[1];
childRegions[0].classCountsIsValid = true;
childRegions[1].classCountsIsValid = true;
// Save the ranges of the child stack entries.
childRegions[0].setRange( region.begin() , region.begin() + bgfunc.min_index_ );
childRegions[0].rule = region.rule;
childRegions[0].rule.push_back(std::make_pair(1, 1.0));
childRegions[1].setRange( region.begin() + bgfunc.min_index_ , region.end() );
childRegions[1].rule = region.rule;
childRegions[1].rule.push_back(std::make_pair(1, 1.0));
//adjust oob ranges
// std::cout << "adjust oob" << std::endl;
//sort the oobs
std::sort(region.oob_begin(), region.oob_end(),
SortSamplesByDimensions< MultiArray<2, double> > (dDistanceFromHyperplane, 0));
//find split index
int nOOBindx;
for(nOOBindx=0; nOOBindx<region.oob_size(); nOOBindx++)
{
if(dDistanceFromHyperplane(region.oob_begin()[nOOBindx],0)>=node.intercept())
break;
}
childRegions[0].set_oob_range( region.oob_begin() , region.oob_begin() + nOOBindx );
childRegions[1].set_oob_range( region.oob_begin() + nOOBindx , region.oob_end() );
// std::cout << "end" << std::endl;
// outm2(region.oob_begin());outm2(nOOBindx);outm(region.oob_begin() + nOOBindx);
//_adjust oob ranges
return i_HyperplaneNode;
}
};
/** Standard ridge regression split
*/
typedef RidgeSplit<BestGiniOfColumn<GiniCriterion> > GiniRidgeSplit;
} //namespace vigra
#endif // VIGRA_RANDOM_FOREST_RIDGE_SPLIT_H
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