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vigra/random_forest/rf_ridge_split.hxx |

    00001 //
    00002 // C++ Interface: rf_ridge_split
    00003 //
    00004 // Description:
    00005 //
    00006 //
    00007 // Author: Nico Splitthoff <splitthoff@zg00103>, (C) 2009
    00008 //
    00009 // Copyright: See COPYING file that comes with this distribution
    00010 //
    00011 //
    00012 #ifndef VIGRA_RANDOM_FOREST_RIDGE_SPLIT_H
    00013 #define VIGRA_RANDOM_FOREST_RIDGE_SPLIT_H
    00014 //#include "rf_sampling.hxx"
    00015 #include "../sampling.hxx"
    00016 #include "rf_split.hxx"
    00017 #include "rf_nodeproxy.hxx"
    00018 #include "../regression.hxx"
    00019
    00020 #define outm(v) std::cout << (#v) << ": " << (v) << std::endl;
    00021 #define outm2(v) std::cout << (#v) << ": " << (v) << ", ";
    00022
    00023 namespace vigra
    00024 {
    00025
    00026 /*template<>
    00027 class Node<i_RegrNode>
    00028 : public NodeBase
    00029 {
    00030 public:
    00031     typedef NodeBase BT;
    00032
    00033
    00034     Node(   BT::T_Container_type &   topology,
    00035         BT::P_Container_type &   param,
    00036             int nNumCols)
    00037     :   BT(5+nNumCols,2+nNumCols,topology, param)
    00038     {
    00039         BT::typeID() = i_RegrNode;
    00040     }
    00041
    00042     Node(   BT::T_Container_type     &   topology,
    00043         BT::P_Container_type     &   param,
    00044         INT                   n             )
    00045     :   BT(5,2,topology, param, n)
    00046     {}
    00047
    00048     Node( BT & node_)
    00049     :   BT(5, 2, node_)
    00050     {}
    00051
    00052     double& threshold()
    00053     {
    00054         return BT::parameters_begin()[1];
    00055     }
    00056
    00057     BT::INT& column()
    00058     {
    00059         return BT::column_data()[0];
    00060     }
    00061
    00062     template<class U, class C>
    00063             BT::INT& next(MultiArrayView<2,U,C> const & feature)
    00064             {
    00065                 return (feature(0, column()) < threshold())? child(0):child(1);
    00066             }
    00067 };*/
    00068
    00069
    00070 template<class ColumnDecisionFunctor, class Tag = ClassificationTag>
    00071 class RidgeSplit: public SplitBase<Tag>
    00072 {
    00073   public:
    00074
    00075
    00076     typedef SplitBase<Tag> SB;
    00077
    00078     ArrayVector<Int32>          splitColumns;
    00079     ColumnDecisionFunctor       bgfunc;
    00080
    00081     double                      region_gini_;
    00082     ArrayVector<double>         min_gini_;
    00083     ArrayVector<std::ptrdiff_t> min_indices_;
    00084     ArrayVector<double>         min_thresholds_;
    00085
    00086     int                         bestSplitIndex;
    00087
    00088     //dns
    00089     bool            m_bDoScalingInTraining;
    00090     bool            m_bDoBestLambdaBasedOnGini;
    00091
    00092     RidgeSplit()
    00093     :m_bDoScalingInTraining(true),
    00094     m_bDoBestLambdaBasedOnGini(true)
    00095     {
    00096     }
    00097
    00098     double minGini() const
    00099     {
    00100         return min_gini_[bestSplitIndex];
    00101     }
    00102
    00103     int bestSplitColumn() const
    00104     {
    00105         return splitColumns[bestSplitIndex];
    00106     }
    00107
    00108     bool& doScalingInTraining()
    00109     { return m_bDoScalingInTraining; }
    00110
    00111     bool& doBestLambdaBasedOnGini()
    00112     { return m_bDoBestLambdaBasedOnGini; }
    00113
    00114     template<class T>
    00115             void set_external_parameters(ProblemSpec<T> const & in)
    00116     {
    00117         SB::set_external_parameters(in);
    00118         bgfunc.set_external_parameters(in);
    00119         int featureCount_ = in.column_count_;
    00120         splitColumns.resize(featureCount_);
    00121         for(int k=0; k<featureCount_; ++k)
    00122             splitColumns[k] = k;
    00123         min_gini_.resize(featureCount_);
    00124         min_indices_.resize(featureCount_);
    00125         min_thresholds_.resize(featureCount_);
    00126     }
    00127
    00128
    00129     template<class T, class C, class T2, class C2, class Region, class Random>
    00130     int findBestSplit(MultiArrayView<2, T, C> features,
    00131                       MultiArrayView<2, T2, C2>  multiClassLabels,
    00132                       Region & region,
    00133                       ArrayVector<Region>& childRegions,
    00134                       Random & randint)
    00135     {
    00136
    00137     //std::cerr << "Split called" << std::endl;
    00138     typedef typename Region::IndexIterator IndexIterator;
    00139     typedef typename MultiArrayView <2, T, C>::difference_type fShape;
    00140     typedef typename MultiArrayView <2, T2, C2>::difference_type lShape;
    00141     typedef typename MultiArrayView <2, double>::difference_type dShape;
    00142
    00143         // calculate things that haven't been calculated yet.
    00144 //    std::cout << "start" << std::endl;
    00145         if(std::accumulate(region.classCounts().begin(),
    00146                            region.classCounts().end(), 0) != region.size())
    00147         {
    00148             RandomForestClassCounter<   MultiArrayView<2,T2, C2>,
    00149                                         ArrayVector<double> >
    00150                 counter(multiClassLabels, region.classCounts());
    00151             std::for_each(  region.begin(), region.end(), counter);
    00152             region.classCountsIsValid = true;
    00153         }
    00154
    00155
    00156         // Is the region pure already?
    00157         region_gini_ = GiniCriterion::impurity(region.classCounts(),
    00158                 region.size());
    00159         if(region_gini_ == 0 || region.size() < SB::ext_param_.actual_mtry_ || region.oob_size() < 2)
    00160             return  SB::makeTerminalNode(features, multiClassLabels, region, randint);
    00161
    00162         // select columns  to be tried.
    00163     for(int ii = 0; ii < SB::ext_param_.actual_mtry_; ++ii)
    00164         std::swap(splitColumns[ii],
    00165             splitColumns[ii+ randint(features.shape(1) - ii)]);
    00166
    00167     //do implicit binary case
    00168     MultiArray<2, T2> labels(lShape(multiClassLabels.shape(0),1));
    00169       //number of classes should be >1, otherwise makeTerminalNode would have been called
    00170       int nNumClasses=0;
    00171       for(int n=0; n<(int)region.classCounts().size(); n++)
    00172         nNumClasses+=((region.classCounts()[n]>0) ? 1:0);
    00173
    00174       //convert to binary case
    00175       if(nNumClasses>2)
    00176       {
    00177         int nMaxClass=0;
    00178         int nMaxClassCounts=0;
    00179         for(int n=0; n<(int)region.classCounts().size(); n++)
    00180         {
    00181           //this should occur in any case:
    00182           //we had more than two non-zero classes in order to get here
    00183           if(region.classCounts()[n]>nMaxClassCounts)
    00184           {
    00185         nMaxClassCounts=region.classCounts()[n];
    00186         nMaxClass=n;
    00187           }
    00188         }
    00189
    00190         //create binary labels
    00191         for(int n=0; n<multiClassLabels.shape(0); n++)
    00192           labels(n,0)=((multiClassLabels(n,0)==nMaxClass) ? 1:0);
    00193       }
    00194       else
    00195         labels=multiClassLabels;
    00196
    00197     //_do implicit binary case
    00198
    00199     //uncomment this for some debugging
    00200 /*  int nNumCases=features.shape(0);
    00201
    00202     typedef typename MultiArrayView <2, int>::difference_type nShape;
    00203     MultiArray<2, int> elementCounterArray(nShape(nNumCases,1),(int)0);
    00204     int nUniqueElements=0;
    00205     for(int n=0; n<region.size(); n++)
    00206         elementCounterArray[region[n]]++;
    00207
    00208     for(int n=0; n<nNumCases; n++)
    00209         nUniqueElements+=((elementCounterArray[n]>0) ? 1:0);
    00210
    00211     outm(nUniqueElements);
    00212     nUniqueElements=0;
    00213     MultiArray<2, int> elementCounterArray_oob(nShape(nNumCases,1),(int)0);
    00214     for(int n=0; n<region.oob_size(); n++)
    00215         elementCounterArray_oob[region.oob_begin()[n]]++;
    00216     for(int n=0; n<nNumCases; n++)
    00217         nUniqueElements+=((elementCounterArray_oob[n]>0) ? 1:0);
    00218     outm(nUniqueElements);
    00219
    00220     int notUniqueElements=0;
    00221     for(int n=0; n<nNumCases; n++)
    00222         notUniqueElements+=(((elementCounterArray_oob[n]>0) && (elementCounterArray[n]>0)) ? 1:0);
    00223     outm(notUniqueElements);*/
    00224
    00225     //outm(SB::ext_param_.actual_mtry_);
    00226
    00227
    00228 //select submatrix of features for regression calculation
    00229     MultiArrayView<2, T, C> cVector;
    00230     MultiArray<2, T> xtrain(fShape(region.size(),SB::ext_param_.actual_mtry_));
    00231     //we only want -1 and 1 for this
    00232     MultiArray<2, double> regrLabels(dShape(region.size(),1));
    00233
    00234     //copy data into a vigra data structure and centre and scale while doing so
    00235     MultiArray<2, double> meanMatrix(dShape(SB::ext_param_.actual_mtry_,1));
    00236     MultiArray<2, double> stdMatrix(dShape(SB::ext_param_.actual_mtry_,1));
    00237     for(int m=0; m<SB::ext_param_.actual_mtry_; m++)
    00238     {
    00239         cVector=columnVector(features, splitColumns[m]);
    00240
    00241         //centre and scale the data
    00242         double dCurrFeatureColumnMean=0.0;
    00243         double dCurrFeatureColumnStd=1.0; //default value
    00244
    00245         //calc mean on bootstrap data
    00246         for(int n=0; n<region.size(); n++)
    00247           dCurrFeatureColumnMean+=cVector[region[n]];
    00248         dCurrFeatureColumnMean/=region.size();
    00249         //calc scaling
    00250         if(m_bDoScalingInTraining)
    00251         {
    00252           for(int n=0; n<region.size(); n++)
    00253           {
    00254               dCurrFeatureColumnStd+=
    00255             (cVector[region[n]]-dCurrFeatureColumnMean)*(cVector[region[n]]-dCurrFeatureColumnMean);
    00256           }
    00257           //unbiased std estimator:
    00258           dCurrFeatureColumnStd=sqrt(dCurrFeatureColumnStd/(region.size()-1));
    00259         }
    00260         //dCurrFeatureColumnStd is still 1.0 if we didn't want scaling
    00261         stdMatrix(m,0)=dCurrFeatureColumnStd;
    00262
    00263         meanMatrix(m,0)=dCurrFeatureColumnMean;
    00264
    00265         //get feature matrix, i.e. A (note that weighting is done automatically
    00266         //since rows can occur multiple times -> bagging)
    00267         for(int n=0; n<region.size(); n++)
    00268             xtrain(n,m)=(cVector[region[n]]-dCurrFeatureColumnMean)/dCurrFeatureColumnStd;
    00269     }
    00270
    00271 //    std::cout << "middle" << std::endl;
    00272     //get label vector (i.e. b)
    00273     for(int n=0; n<region.size(); n++)
    00274     {
    00275         //we checked for/built binary case further up.
    00276         //class labels should thus be either 0 or 1
    00277         //-> convert to -1 and 1 for regression
    00278         regrLabels(n,0)=((labels[region[n]]==0) ? -1:1);
    00279     }
    00280
    00281     MultiArray<2, double> dLambdas(dShape(11,1));
    00282     int nCounter=0;
    00283     for(int nLambda=-5; nLambda<=5; nLambda++)
    00284         dLambdas[nCounter++]=pow(10.0,nLambda);
    00285     //destination vector for regression coefficients; use same type as for xtrain
    00286     MultiArray<2, double> regrCoef(dShape(SB::ext_param_.actual_mtry_,11));
    00287     ridgeRegressionSeries(xtrain,regrLabels,regrCoef,dLambdas);
    00288
    00289     double dMaxRidgeSum=NumericTraits<double>::min();
    00290     double dCurrRidgeSum;
    00291     int nMaxRidgeSumAtLambdaInd=0;
    00292
    00293     for(int nLambdaInd=0; nLambdaInd<11; nLambdaInd++)
    00294     {
    00295         //just sum up the correct answers
    00296         //(correct means >=intercept for class 1, <intercept for class 0)
    00297         //(intercept=0 or intercept=threshold based on gini)
    00298         dCurrRidgeSum=0.0;
    00299
    00300         //assemble projection vector
    00301         MultiArray<2, double> dDistanceFromHyperplane(dShape(features.shape(0),1));
    00302
    00303         for(int n=0; n<region.oob_size(); n++)
    00304         {
    00305           dDistanceFromHyperplane(region.oob_begin()[n],0)=0.0;
    00306           for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
    00307           {
    00308             dDistanceFromHyperplane(region.oob_begin()[n],0)+=
    00309               features(region.oob_begin()[n],splitColumns[m])*regrCoef(m,nLambdaInd);
    00310           }
    00311         }
    00312
    00313         double dCurrIntercept=0.0;
    00314         if(m_bDoBestLambdaBasedOnGini)
    00315         {
    00316           //calculate gini index
    00317           bgfunc(dDistanceFromHyperplane,
    00318               labels,
    00319               region.oob_begin(), region.oob_end(),
    00320               region.classCounts());
    00321           dCurrIntercept=bgfunc.min_threshold_;
    00322         }
    00323         else
    00324         {
    00325           for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
    00326             dCurrIntercept+=meanMatrix(m,0)*regrCoef(m,nLambdaInd);
    00327         }
    00328
    00329         for(int n=0; n<region.oob_size(); n++)
    00330         {
    00331             //check what lambda performs best on oob data
    00332             int nClassPrediction=((dDistanceFromHyperplane(region.oob_begin()[n],0) >=dCurrIntercept) ? 1:0);
    00333             dCurrRidgeSum+=((nClassPrediction == labels(region.oob_begin()[n],0)) ? 1:0);
    00334         }
    00335         if(dCurrRidgeSum>dMaxRidgeSum)
    00336         {
    00337             dMaxRidgeSum=dCurrRidgeSum;
    00338             nMaxRidgeSumAtLambdaInd=nLambdaInd;
    00339         }
    00340     }
    00341
    00342 //    std::cout << "middle2" << std::endl;
    00343         //create a Node for output
    00344         Node<i_HyperplaneNode>   node(SB::ext_param_.actual_mtry_, SB::t_data, SB::p_data);
    00345
    00346     //normalise coeffs
    00347         //data was scaled (by 1.0 or by std) -> take into account
    00348         MultiArray<2, double> dCoeffVector(dShape(SB::ext_param_.actual_mtry_,1));
    00349         for(int n=0; n<SB::ext_param_.actual_mtry_; n++)
    00350           dCoeffVector(n,0)=regrCoef(n,nMaxRidgeSumAtLambdaInd)*stdMatrix(n,0);
    00351
    00352         //calc norm
    00353         double dVnorm=columnVector(regrCoef,nMaxRidgeSumAtLambdaInd).norm();
    00354
    00355         for(int n=0; n<SB::ext_param_.actual_mtry_; n++)
    00356             node.weights()[n]=dCoeffVector(n,0)/dVnorm;
    00357     //_normalise coeffs
    00358
    00359     //save the columns
    00360         node.column_data()[0]=SB::ext_param_.actual_mtry_;
    00361         for(int n=0; n<SB::ext_param_.actual_mtry_; n++)
    00362             node.column_data()[n+1]=splitColumns[n];
    00363
    00364     //assemble projection vector
    00365         //careful here: "region" is a pointer to indices...
    00366         //all the indices in "region" need to have valid data
    00367         //convert from "region" space to original "feature" space
    00368         MultiArray<2, double> dDistanceFromHyperplane(dShape(features.shape(0),1));
    00369
    00370         for(int n=0; n<region.size(); n++)
    00371         {
    00372             dDistanceFromHyperplane(region[n],0)=0.0;
    00373             for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
    00374             {
    00375               dDistanceFromHyperplane(region[n],0)+=
    00376                features(region[n],m)*node.weights()[m];
    00377             }
    00378         }
    00379         for(int n=0; n<region.oob_size(); n++)
    00380         {
    00381             dDistanceFromHyperplane(region.oob_begin()[n],0)=0.0;
    00382             for (int m=0; m<SB::ext_param_.actual_mtry_; m++)
    00383             {
    00384               dDistanceFromHyperplane(region.oob_begin()[n],0)+=
    00385             features(region.oob_begin()[n],m)*node.weights()[m];
    00386             }
    00387         }
    00388
    00389     //calculate gini index
    00390         bgfunc(dDistanceFromHyperplane,
    00391             labels,
    00392             region.begin(), region.end(),
    00393             region.classCounts());
    00394
    00395         // did not find any suitable split
    00396     if(closeAtTolerance(bgfunc.min_gini_, NumericTraits<double>::max()))
    00397         return  SB::makeTerminalNode(features, multiClassLabels, region, randint);
    00398
    00399     //take gini threshold here due to scaling, normalisation, etc. of the coefficients
    00400     node.intercept()    = bgfunc.min_threshold_;
    00401     SB::node_ = node;
    00402
    00403     childRegions[0].classCounts() = bgfunc.bestCurrentCounts[0];
    00404     childRegions[1].classCounts() = bgfunc.bestCurrentCounts[1];
    00405     childRegions[0].classCountsIsValid = true;
    00406     childRegions[1].classCountsIsValid = true;
    00407
    00408         // Save the ranges of the child stack entries.
    00409     childRegions[0].setRange(   region.begin()  , region.begin() + bgfunc.min_index_   );
    00410     childRegions[0].rule = region.rule;
    00411     childRegions[0].rule.push_back(std::make_pair(1, 1.0));
    00412     childRegions[1].setRange(   region.begin() + bgfunc.min_index_       , region.end()    );
    00413     childRegions[1].rule = region.rule;
    00414     childRegions[1].rule.push_back(std::make_pair(1, 1.0));
    00415
    00416     //adjust oob ranges
    00417 //    std::cout << "adjust oob" << std::endl;
    00418     //sort the oobs
    00419       std::sort(region.oob_begin(), region.oob_end(),
    00420             SortSamplesByDimensions< MultiArray<2, double> > (dDistanceFromHyperplane, 0));
    00421
    00422       //find split index
    00423       int nOOBindx;
    00424       for(nOOBindx=0; nOOBindx<region.oob_size(); nOOBindx++)
    00425       {
    00426         if(dDistanceFromHyperplane(region.oob_begin()[nOOBindx],0)>=node.intercept())
    00427           break;
    00428       }
    00429
    00430       childRegions[0].set_oob_range(   region.oob_begin()  , region.oob_begin() + nOOBindx   );
    00431       childRegions[1].set_oob_range(   region.oob_begin() + nOOBindx , region.oob_end() );
    00432
    00433 //    std::cout << "end" << std::endl;
    00434 //    outm2(region.oob_begin());outm2(nOOBindx);outm(region.oob_begin() + nOOBindx);
    00435     //_adjust oob ranges
    00436
    00437     return i_HyperplaneNode;
    00438     }
    00439 };
    00440
    00441 /** Standard ridge regression split
    00442  */
    00443 typedef RidgeSplit<BestGiniOfColumn<GiniCriterion> >  GiniRidgeSplit;
    00444
    00445
    00446 } //namespace vigra
    00447 #endif // VIGRA_RANDOM_FOREST_RIDGE_SPLIT_H

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)