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    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*                  Copyright 2008 by Ullrich Koethe                    */
    00004 /*                                                                      */
    00005 /*    This file is part of the VIGRA computer vision library.           */
    00006 /*    The VIGRA Website is                                              */
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    00009 /*        ullrich.koethe@iwr.uni-heidelberg.de    or                    */
    00010 /*        vigra@informatik.uni-hamburg.de                               */
    00011 /*                                                                      */
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    00034 /************************************************************************/
    00035
    00036 #ifndef VIGRA_RANDOM_FOREST_DEPREC_HXX
    00037 #define VIGRA_RANDOM_FOREST_DEPREC_HXX
    00038
    00039 #include <algorithm>
    00040 #include <map>
    00041 #include <numeric>
    00042 #include <iostream>
    00043 #include <ctime>
    00044 #include <cstdlib>
    00045 #include "vigra/mathutil.hxx"
    00046 #include "vigra/array_vector.hxx"
    00047 #include "vigra/sized_int.hxx"
    00048 #include "vigra/matrix.hxx"
    00049 #include "vigra/random.hxx"
    00050 #include "vigra/functorexpression.hxx"
    00051
    00052
    00053 namespace vigra
    00054 {
    00055
    00056 /** \addtogroup MachineLearning
    00057 **/
    00058 //@{
    00059
    00060 namespace detail
    00061 {
    00062
    00063 template<class DataMatrix>
    00064 class RandomForestDeprecFeatureSorter
    00065 {
    00066     DataMatrix const & data_;
    00067     MultiArrayIndex sortColumn_;
    00068
    00069   public:
    00070
    00071     RandomForestDeprecFeatureSorter(DataMatrix const & data, MultiArrayIndex sortColumn)
    00072     : data_(data),
    00073       sortColumn_(sortColumn)
    00074     {}
    00075
    00076     void setColumn(MultiArrayIndex sortColumn)
    00077     {
    00078         sortColumn_ = sortColumn;
    00079     }
    00080
    00081     bool operator()(MultiArrayIndex l, MultiArrayIndex r) const
    00082     {
    00083         return data_(l, sortColumn_) < data_(r, sortColumn_);
    00084     }
    00085 };
    00086
    00087 template<class LabelArray>
    00088 class RandomForestDeprecLabelSorter
    00089 {
    00090     LabelArray const & labels_;
    00091
    00092   public:
    00093
    00094     RandomForestDeprecLabelSorter(LabelArray const & labels)
    00095     : labels_(labels)
    00096     {}
    00097
    00098     bool operator()(MultiArrayIndex l, MultiArrayIndex r) const
    00099     {
    00100         return labels_[l] < labels_[r];
    00101     }
    00102 };
    00103
    00104 template <class CountArray>
    00105 class RandomForestDeprecClassCounter
    00106 {
    00107     ArrayVector<int> const & labels_;
    00108     CountArray & counts_;
    00109
    00110   public:
    00111
    00112     RandomForestDeprecClassCounter(ArrayVector<int> const & labels, CountArray & counts)
    00113     : labels_(labels),
    00114       counts_(counts)
    00115     {
    00116         reset();
    00117     }
    00118
    00119     void reset()
    00120     {
    00121         counts_.init(0);
    00122     }
    00123
    00124     void operator()(MultiArrayIndex l) const
    00125     {
    00126         ++counts_[labels_[l]];
    00127     }
    00128 };
    00129
    00130 struct DecisionTreeDeprecCountNonzeroFunctor
    00131 {
    00132     double operator()(double old, double other) const
    00133     {
    00134         if(other != 0.0)
    00135             ++old;
    00136         return old;
    00137     }
    00138 };
    00139
    00140 struct DecisionTreeDeprecNode
    00141 {
    00142     DecisionTreeDeprecNode(int t, MultiArrayIndex bestColumn)
    00143     : thresholdIndex(t), splitColumn(bestColumn)
    00144     {}
    00145
    00146     int children[2];
    00147     int thresholdIndex;
    00148     Int32 splitColumn;
    00149 };
    00150
    00151 template <class INT>
    00152 struct DecisionTreeDeprecNodeProxy
    00153 {
    00154     DecisionTreeDeprecNodeProxy(ArrayVector<INT> const & tree, INT n)
    00155     : node(const_cast<ArrayVector<INT> &>(tree).begin()+n)
    00156     {}
    00157
    00158     INT & child(INT l) const
    00159     {
    00160         return node[l];
    00161     }
    00162
    00163     INT & decisionWeightsIndex() const
    00164     {
    00165         return node[2];
    00166     }
    00167
    00168     typename ArrayVector<INT>::iterator decisionColumns() const
    00169     {
    00170         return node+3;
    00171     }
    00172
    00173     mutable typename ArrayVector<INT>::iterator node;
    00174 };
    00175
    00176 struct DecisionTreeDeprecAxisSplitFunctor
    00177 {
    00178     ArrayVector<Int32> splitColumns;
    00179     ArrayVector<double> classCounts, currentCounts[2], bestCounts[2], classWeights;
    00180     double threshold;
    00181     double totalCounts[2], bestTotalCounts[2];
    00182     int mtry, classCount, bestSplitColumn;
    00183     bool pure[2], isWeighted;
    00184
    00185     void init(int mtry, int cols, int classCount, ArrayVector<double> const & weights)
    00186     {
    00187         this->mtry = mtry;
    00188         splitColumns.resize(cols);
    00189         for(int k=0; k<cols; ++k)
    00190             splitColumns[k] = k;
    00191
    00192         this->classCount = classCount;
    00193         classCounts.resize(classCount);
    00194         currentCounts[0].resize(classCount);
    00195         currentCounts[1].resize(classCount);
    00196         bestCounts[0].resize(classCount);
    00197         bestCounts[1].resize(classCount);
    00198
    00199         isWeighted = weights.size() > 0;
    00200         if(isWeighted)
    00201             classWeights = weights;
    00202         else
    00203             classWeights.resize(classCount, 1.0);
    00204     }
    00205
    00206     bool isPure(int k) const
    00207     {
    00208         return pure[k];
    00209     }
    00210
    00211     unsigned int totalCount(int k) const
    00212     {
    00213         return (unsigned int)bestTotalCounts[k];
    00214     }
    00215
    00216     int sizeofNode() const { return 4; }
    00217
    00218     int writeSplitParameters(ArrayVector<Int32> & tree,
    00219                                 ArrayVector<double> &terminalWeights)
    00220     {
    00221         int currentWeightIndex = terminalWeights.size();
    00222         terminalWeights.push_back(threshold);
    00223
    00224         int currentNodeIndex = tree.size();
    00225         tree.push_back(-1);  // left child
    00226         tree.push_back(-1);  // right child
    00227         tree.push_back(currentWeightIndex);
    00228         tree.push_back(bestSplitColumn);
    00229
    00230         return currentNodeIndex;
    00231     }
    00232
    00233     void writeWeights(int l, ArrayVector<double> &terminalWeights)
    00234     {
    00235         for(int k=0; k<classCount; ++k)
    00236             terminalWeights.push_back(isWeighted
    00237                                            ? bestCounts[l][k]
    00238                                            : bestCounts[l][k] / totalCount(l));
    00239     }
    00240
    00241     template <class U, class C, class AxesIterator, class WeightIterator>
    00242     bool decideAtNode(MultiArrayView<2, U, C> const & features,
    00243                       AxesIterator a, WeightIterator w) const
    00244     {
    00245         return (features(0, *a) < *w);
    00246     }
    00247
    00248     template <class U, class C, class IndexIterator, class Random>
    00249     IndexIterator findBestSplit(MultiArrayView<2, U, C> const & features,
    00250                                 ArrayVector<int> const & labels,
    00251                                 IndexIterator indices, int exampleCount,
    00252                                 Random & randint);
    00253
    00254 };
    00255
    00256
    00257 template <class U, class C, class IndexIterator, class Random>
    00258 IndexIterator
    00259 DecisionTreeDeprecAxisSplitFunctor::findBestSplit(MultiArrayView<2, U, C> const & features,
    00260                                             ArrayVector<int> const & labels,
    00261                                             IndexIterator indices, int exampleCount,
    00262                                             Random & randint)
    00263 {
    00264     // select columns to be tried for split
    00265     for(int k=0; k<mtry; ++k)
    00266         std::swap(splitColumns[k], splitColumns[k+randint(columnCount(features)-k)]);
    00267
    00268     RandomForestDeprecFeatureSorter<MultiArrayView<2, U, C> > sorter(features, 0);
    00269     RandomForestDeprecClassCounter<ArrayVector<double> > counter(labels, classCounts);
    00270     std::for_each(indices, indices+exampleCount, counter);
    00271
    00272     // find the best gini index
    00273     double minGini = NumericTraits<double>::max();
    00274     IndexIterator bestSplit = indices;
    00275     for(int k=0; k<mtry; ++k)
    00276     {
    00277         sorter.setColumn(splitColumns[k]);
    00278         std::sort(indices, indices+exampleCount, sorter);
    00279
    00280         currentCounts[0].init(0);
    00281         std::transform(classCounts.begin(), classCounts.end(), classWeights.begin(),
    00282                        currentCounts[1].begin(), std::multiplies<double>());
    00283         totalCounts[0] = 0;
    00284         totalCounts[1] = std::accumulate(currentCounts[1].begin(), currentCounts[1].end(), 0.0);
    00285         for(int m = 0; m < exampleCount-1; ++m)
    00286         {
    00287             int label = labels[indices[m]];
    00288             double w = classWeights[label];
    00289             currentCounts[0][label] += w;
    00290             totalCounts[0] += w;
    00291             currentCounts[1][label] -= w;
    00292             totalCounts[1] -= w;
    00293
    00294             if (m < exampleCount-2 &&
    00295                 features(indices[m], splitColumns[k]) == features(indices[m+1], splitColumns[k]))
    00296                 continue ;
    00297
    00298             double gini = 0.0;
    00299             if(classCount == 2)
    00300             {
    00301                 gini = currentCounts[0][0]*currentCounts[0][1] / totalCounts[0] +
    00302                        currentCounts[1][0]*currentCounts[1][1] / totalCounts[1];
    00303             }
    00304             else
    00305             {
    00306                 for(int l=0; l<classCount; ++l)
    00307                     gini += currentCounts[0][l]*(1.0 - currentCounts[0][l] / totalCounts[0]) +
    00308                             currentCounts[1][l]*(1.0 - currentCounts[1][l] / totalCounts[1]);
    00309             }
    00310             if(gini < minGini)
    00311             {
    00312                 minGini = gini;
    00313                 bestSplit = indices+m;
    00314                 bestSplitColumn = splitColumns[k];
    00315                 bestCounts[0] = currentCounts[0];
    00316                 bestCounts[1] = currentCounts[1];
    00317             }
    00318         }
    00319
    00320
    00321
    00322     }
    00323         //std::cerr << minGini << " " << bestSplitColumn << std::endl;
    00324     // split using the best feature
    00325     sorter.setColumn(bestSplitColumn);
    00326     std::sort(indices, indices+exampleCount, sorter);
    00327
    00328     for(int k=0; k<2; ++k)
    00329     {
    00330         bestTotalCounts[k] = std::accumulate(bestCounts[k].begin(), bestCounts[k].end(), 0.0);
    00331     }
    00332
    00333     threshold = (features(bestSplit[0], bestSplitColumn) + features(bestSplit[1], bestSplitColumn)) / 2.0;
    00334     ++bestSplit;
    00335
    00336     counter.reset();
    00337     std::for_each(indices, bestSplit, counter);
    00338     pure[0] = 1.0 == std::accumulate(classCounts.begin(), classCounts.end(), 0.0, DecisionTreeDeprecCountNonzeroFunctor());
    00339     counter.reset();
    00340     std::for_each(bestSplit, indices+exampleCount, counter);
    00341     pure[1] = 1.0 == std::accumulate(classCounts.begin(), classCounts.end(), 0.0, DecisionTreeDeprecCountNonzeroFunctor());
    00342
    00343     return bestSplit;
    00344 }
    00345
    00346 enum  { DecisionTreeDeprecNoParent = -1 };
    00347
    00348 template <class Iterator>
    00349 struct DecisionTreeDeprecStackEntry
    00350 {
    00351     DecisionTreeDeprecStackEntry(Iterator i, int c,
    00352                            int lp = DecisionTreeDeprecNoParent, int rp = DecisionTreeDeprecNoParent)
    00353     : indices(i), exampleCount(c),
    00354       leftParent(lp), rightParent(rp)
    00355     {}
    00356
    00357     Iterator indices;
    00358     int exampleCount, leftParent, rightParent;
    00359 };
    00360
    00361 class DecisionTreeDeprec
    00362 {
    00363   public:
    00364     typedef Int32 TreeInt;
    00365     ArrayVector<TreeInt>  tree_;
    00366     ArrayVector<double> terminalWeights_;
    00367     unsigned int classCount_;
    00368     DecisionTreeDeprecAxisSplitFunctor split;
    00369
    00370   public:
    00371
    00372
    00373     DecisionTreeDeprec(unsigned int classCount)
    00374     : classCount_(classCount)
    00375     {}
    00376
    00377     void reset(unsigned int classCount = 0)
    00378     {
    00379         if(classCount)
    00380             classCount_ = classCount;
    00381         tree_.clear();
    00382         terminalWeights_.clear();
    00383     }
    00384
    00385     template <class U, class C, class Iterator, class Options, class Random>
    00386     void learn(MultiArrayView<2, U, C> const & features,
    00387                ArrayVector<int> const & labels,
    00388                Iterator indices, int exampleCount,
    00389                Options const & options,
    00390                Random & randint);
    00391
    00392     template <class U, class C>
    00393     ArrayVector<double>::const_iterator
    00394     predict(MultiArrayView<2, U, C> const & features) const
    00395     {
    00396         int nodeindex = 0;
    00397         for(;;)
    00398         {
    00399             DecisionTreeDeprecNodeProxy<TreeInt> node(tree_, nodeindex);
    00400             nodeindex = split.decideAtNode(features, node.decisionColumns(),
    00401                                        terminalWeights_.begin() + node.decisionWeightsIndex())
    00402                                 ? node.child(0)
    00403                                 : node.child(1);
    00404             if(nodeindex <= 0)
    00405                 return terminalWeights_.begin() + (-nodeindex);
    00406         }
    00407     }
    00408
    00409     template <class U, class C>
    00410     int
    00411     predictLabel(MultiArrayView<2, U, C> const & features) const
    00412     {
    00413         ArrayVector<double>::const_iterator weights = predict(features);
    00414         return argMax(weights, weights+classCount_) - weights;
    00415     }
    00416
    00417     template <class U, class C>
    00418     int
    00419     leafID(MultiArrayView<2, U, C> const & features) const
    00420     {
    00421         int nodeindex = 0;
    00422         for(;;)
    00423         {
    00424             DecisionTreeDeprecNodeProxy<TreeInt> node(tree_, nodeindex);
    00425             nodeindex = split.decideAtNode(features, node.decisionColumns(),
    00426                                        terminalWeights_.begin() + node.decisionWeightsIndex())
    00427                                 ? node.child(0)
    00428                                 : node.child(1);
    00429             if(nodeindex <= 0)
    00430                 return -nodeindex;
    00431         }
    00432     }
    00433
    00434     void depth(int & maxDep, int & interiorCount, int & leafCount, int k = 0, int d = 1) const
    00435     {
    00436         DecisionTreeDeprecNodeProxy<TreeInt> node(tree_, k);
    00437         ++interiorCount;
    00438         ++d;
    00439         for(int l=0; l<2; ++l)
    00440         {
    00441             int child = node.child(l);
    00442             if(child > 0)
    00443                 depth(maxDep, interiorCount, leafCount, child, d);
    00444             else
    00445             {
    00446                 ++leafCount;
    00447                 if(maxDep < d)
    00448                     maxDep = d;
    00449             }
    00450         }
    00451     }
    00452
    00453     void printStatistics(std::ostream & o) const
    00454     {
    00455         int maxDep = 0, interiorCount = 0, leafCount = 0;
    00456         depth(maxDep, interiorCount, leafCount);
    00457
    00458         o << "interior nodes: " << interiorCount <<
    00459              ", terminal nodes: " << leafCount <<
    00460              ", depth: " << maxDep << "\n";
    00461     }
    00462
    00463     void print(std::ostream & o, int k = 0, std::string s = "") const
    00464     {
    00465         DecisionTreeDeprecNodeProxy<TreeInt> node(tree_, k);
    00466         o << s << (*node.decisionColumns()) << " " << terminalWeights_[node.decisionWeightsIndex()] << "\n";
    00467
    00468         for(int l=0; l<2; ++l)
    00469         {
    00470             int child = node.child(l);
    00471             if(child <= 0)
    00472                 o << s << " weights " << terminalWeights_[-child] << " "
    00473                                       << terminalWeights_[-child+1] << "\n";
    00474             else
    00475                 print(o, child, s+" ");
    00476         }
    00477     }
    00478 };
    00479
    00480
    00481 template <class U, class C, class Iterator, class Options, class Random>
    00482 void DecisionTreeDeprec::learn(MultiArrayView<2, U, C> const & features,
    00483                           ArrayVector<int> const & labels,
    00484                           Iterator indices, int exampleCount,
    00485                           Options const & options,
    00486                           Random & randint)
    00487 {
    00488     ArrayVector<double> const & classLoss = options.class_weights;
    00489
    00490     vigra_precondition(classLoss.size() == 0 || classLoss.size() == classCount_,
    00491         "DecisionTreeDeprec2::learn(): class weights array has wrong size.");
    00492
    00493     reset();
    00494
    00495     unsigned int mtry = options.mtry;
    00496     MultiArrayIndex cols = columnCount(features);
    00497
    00498     split.init(mtry, cols, classCount_, classLoss);
    00499
    00500     typedef DecisionTreeDeprecStackEntry<Iterator> Entry;
    00501     ArrayVector<Entry> stack;
    00502     stack.push_back(Entry(indices, exampleCount));
    00503
    00504     while(!stack.empty())
    00505     {
    00506 //        std::cerr << "*";
    00507         indices = stack.back().indices;
    00508         exampleCount = stack.back().exampleCount;
    00509         int leftParent  = stack.back().leftParent,
    00510             rightParent = stack.back().rightParent;
    00511
    00512         stack.pop_back();
    00513
    00514         Iterator bestSplit = split.findBestSplit(features, labels, indices, exampleCount, randint);
    00515
    00516
    00517         int currentNode = split.writeSplitParameters(tree_, terminalWeights_);
    00518
    00519         if(leftParent != DecisionTreeDeprecNoParent)
    00520             DecisionTreeDeprecNodeProxy<TreeInt>(tree_, leftParent).child(0) = currentNode;
    00521         if(rightParent != DecisionTreeDeprecNoParent)
    00522             DecisionTreeDeprecNodeProxy<TreeInt>(tree_, rightParent).child(1) = currentNode;
    00523         leftParent = currentNode;
    00524         rightParent = DecisionTreeDeprecNoParent;
    00525
    00526         for(int l=0; l<2; ++l)
    00527         {
    00528
    00529             if(!split.isPure(l) && split.totalCount(l) >= options.min_split_node_size)
    00530             {
    00531                 // sample is still large enough and not yet perfectly separated => split
    00532                 stack.push_back(Entry(indices, split.totalCount(l), leftParent, rightParent));
    00533             }
    00534             else
    00535             {
    00536                 DecisionTreeDeprecNodeProxy<TreeInt>(tree_, currentNode).child(l) = -(TreeInt)terminalWeights_.size();
    00537
    00538                 split.writeWeights(l, terminalWeights_);
    00539             }
    00540             std::swap(leftParent, rightParent);
    00541             indices = bestSplit;
    00542         }
    00543     }
    00544 //    std::cerr << "\n";
    00545 }
    00546
    00547 } // namespace detail
    00548
    00549 class RandomForestOptionsDeprec
    00550 {
    00551   public:
    00552         /** Initialize all options with default values.
    00553         */
    00554     RandomForestOptionsDeprec()
    00555     : training_set_proportion(1.0),
    00556       mtry(0),
    00557       min_split_node_size(1),
    00558       training_set_size(0),
    00559       sample_with_replacement(true),
    00560       sample_classes_individually(false),
    00561       treeCount(255)
    00562     {}
    00563
    00564         /** Number of features considered in each node.
    00565
    00566             If \a n is 0 (the default), the number of features tried in every node
    00567             is determined by the square root of the total number of features.
    00568             According to Breiman, this quantity should always be optimized by means
    00569             of the out-of-bag error.<br>
    00570             Default: 0 (use <tt>sqrt(columnCount(featureMatrix))</tt>)
    00571         */
    00572     RandomForestOptionsDeprec & featuresPerNode(unsigned int n)
    00573     {
    00574         mtry = n;
    00575         return *this;
    00576     }
    00577
    00578         /** How to sample the subset of the training data for each tree.
    00579
    00580             Each tree is only trained with a subset of the entire training data.
    00581             If \a r is <tt>true</tt>, this subset is sampled from the entire training set with
    00582             replacement.<br>
    00583             Default: <tt>true</tt> (use sampling with replacement))
    00584         */
    00585     RandomForestOptionsDeprec & sampleWithReplacement(bool r)
    00586     {
    00587         sample_with_replacement = r;
    00588         return *this;
    00589     }
    00590
    00591     RandomForestOptionsDeprec & setTreeCount(unsigned int cnt)
    00592     {
    00593         treeCount = cnt;
    00594         return *this;
    00595     }
    00596         /** Proportion of training examples used for each tree.
    00597
    00598             If \a p is 1.0 (the default), and samples are drawn with replacement,
    00599             the training set of each tree will contain as many examples as the entire
    00600             training set, but some are drawn multiply and others not at all. On average,
    00601             each tree is actually trained on about 65% of the examples in the full
    00602             training set. Changing the proportion makes mainly sense when
    00603             sampleWithReplacement() is set to <tt>false</tt>. trainingSetSizeProportional() gets
    00604             overridden by trainingSetSizeAbsolute().<br>
    00605             Default: 1.0
    00606         */
    00607     RandomForestOptionsDeprec & trainingSetSizeProportional(double p)
    00608     {
    00609         vigra_precondition(p >= 0.0 && p <= 1.0,
    00610             "RandomForestOptionsDeprec::trainingSetSizeProportional(): proportion must be in [0, 1].");
    00611         if(training_set_size == 0) // otherwise, absolute size gets priority
    00612             training_set_proportion = p;
    00613         return *this;
    00614     }
    00615
    00616         /** Size of the training set for each tree.
    00617
    00618             If this option is set, it overrides the proportion set by
    00619             trainingSetSizeProportional(). When classes are sampled individually,
    00620             the number of examples is divided by the number of classes (rounded upwards)
    00621             to determine the number of examples drawn from every class.<br>
    00622             Default: <tt>0</tt> (determine size by proportion)
    00623         */
    00624     RandomForestOptionsDeprec & trainingSetSizeAbsolute(unsigned int s)
    00625     {
    00626         training_set_size = s;
    00627         if(s > 0)
    00628             training_set_proportion = 0.0;
    00629         return *this;
    00630     }
    00631
    00632         /** Are the classes sampled individually?
    00633
    00634             If \a s is <tt>false</tt> (the default), the training set for each tree is sampled
    00635             without considering class labels. Otherwise, samples are drawn from each
    00636             class independently. The latter is especially useful in connection
    00637             with the specification of an absolute training set size: then, the same number of
    00638             examples is drawn from every class. This can be used as a counter-measure when the
    00639             classes are very unbalanced in size.<br>
    00640             Default: <tt>false</tt>
    00641         */
    00642     RandomForestOptionsDeprec & sampleClassesIndividually(bool s)
    00643     {
    00644         sample_classes_individually = s;
    00645         return *this;
    00646     }
    00647
    00648         /** Number of examples required for a node to be split.
    00649
    00650             When the number of examples in a node is below this number, the node is not
    00651             split even if class separation is not yet perfect. Instead, the node returns
    00652             the proportion of each class (among the remaining examples) during the
    00653             prediction phase.<br>
    00654             Default: 1 (complete growing)
    00655         */
    00656     RandomForestOptionsDeprec & minSplitNodeSize(unsigned int n)
    00657     {
    00658         if(n == 0)
    00659             n = 1;
    00660         min_split_node_size = n;
    00661         return *this;
    00662     }
    00663
    00664         /** Use a weighted random forest.
    00665
    00666             This is usually used to penalize the errors for the minority class.
    00667             Weights must be convertible to <tt>double</tt>, and the array of weights
    00668             must contain as many entries as there are classes.<br>
    00669             Default: do not use weights
    00670         */
    00671     template <class WeightIterator>
    00672     RandomForestOptionsDeprec & weights(WeightIterator weights, unsigned int classCount)
    00673     {
    00674         class_weights.clear();
    00675         if(weights != 0)
    00676             class_weights.insert(weights, classCount);
    00677         return *this;
    00678     }
    00679
    00680     RandomForestOptionsDeprec & oobData(MultiArrayView<2, UInt8>& data)
    00681     {
    00682         oob_data =data;
    00683         return *this;
    00684     }
    00685
    00686     MultiArrayView<2, UInt8> oob_data;
    00687     ArrayVector<double> class_weights;
    00688     double training_set_proportion;
    00689     unsigned int mtry, min_split_node_size, training_set_size;
    00690     bool sample_with_replacement, sample_classes_individually;
    00691     unsigned int treeCount;
    00692 };
    00693
    00694 /*****************************************************************/
    00695 /*                                                               */
    00696 /*                          RandomForestDeprec                   */
    00697 /*                                                               */
    00698 /*****************************************************************/
    00699
    00700 template <class ClassLabelType>
    00701 class RandomForestDeprec
    00702 {
    00703   public:
    00704     ArrayVector<ClassLabelType> classes_;
    00705     ArrayVector<detail::DecisionTreeDeprec> trees_;
    00706     MultiArrayIndex columnCount_;
    00707     RandomForestOptionsDeprec options_;
    00708
    00709   public:
    00710
    00711     //First two constructors are straight forward.
    00712     //they take either the iterators to an Array of Classlabels or the values
    00713     template<class ClassLabelIterator>
    00714     RandomForestDeprec(ClassLabelIterator cl, ClassLabelIterator cend,
    00715                   unsigned int treeCount = 255,
    00716                   RandomForestOptionsDeprec const & options = RandomForestOptionsDeprec())
    00717     : classes_(cl, cend),
    00718       trees_(treeCount, detail::DecisionTreeDeprec(classes_.size())),
    00719       columnCount_(0),
    00720       options_(options)
    00721     {
    00722         vigra_precondition(options.training_set_proportion == 0.0 ||
    00723                            options.training_set_size == 0,
    00724             "RandomForestOptionsDeprec: absolute and proportional training set sizes "
    00725             "cannot be specified at the same time.");
    00726         vigra_precondition(classes_.size() > 1,
    00727             "RandomForestOptionsDeprec::weights(): need at least two classes.");
    00728         vigra_precondition(options.class_weights.size() == 0 || options.class_weights.size() == classes_.size(),
    00729             "RandomForestOptionsDeprec::weights(): wrong number of classes.");
    00730     }
    00731
    00732     RandomForestDeprec(ClassLabelType const & c1, ClassLabelType const & c2,
    00733                   unsigned int treeCount = 255,
    00734                   RandomForestOptionsDeprec const & options = RandomForestOptionsDeprec())
    00735     : classes_(2),
    00736       trees_(treeCount, detail::DecisionTreeDeprec(2)),
    00737       columnCount_(0),
    00738       options_(options)
    00739     {
    00740         vigra_precondition(options.class_weights.size() == 0 || options.class_weights.size() == 2,
    00741             "RandomForestOptionsDeprec::weights(): wrong number of classes.");
    00742         classes_[0] = c1;
    00743         classes_[1] = c2;
    00744     }
    00745     //This is esp. For the CrosValidator Class
    00746     template<class ClassLabelIterator>
    00747     RandomForestDeprec(ClassLabelIterator cl, ClassLabelIterator cend,
    00748                   RandomForestOptionsDeprec const & options )
    00749     : classes_(cl, cend),
    00750       trees_(options.treeCount , detail::DecisionTreeDeprec(classes_.size())),
    00751       columnCount_(0),
    00752       options_(options)
    00753     {
    00754
    00755         vigra_precondition(options.training_set_proportion == 0.0 ||
    00756                            options.training_set_size == 0,
    00757             "RandomForestOptionsDeprec: absolute and proportional training set sizes "
    00758             "cannot be specified at the same time.");
    00759         vigra_precondition(classes_.size() > 1,
    00760             "RandomForestOptionsDeprec::weights(): need at least two classes.");
    00761         vigra_precondition(options.class_weights.size() == 0 || options.class_weights.size() == classes_.size(),
    00762             "RandomForestOptionsDeprec::weights(): wrong number of classes.");
    00763     }
    00764
    00765     //Not understood yet
    00766     //Does not use the options object but the columnCount object.
    00767     template<class ClassLabelIterator, class TreeIterator, class WeightIterator>
    00768     RandomForestDeprec(ClassLabelIterator cl, ClassLabelIterator cend,
    00769                   unsigned int treeCount, unsigned int columnCount,
    00770                   TreeIterator trees, WeightIterator weights)
    00771     : classes_(cl, cend),
    00772       trees_(treeCount, detail::DecisionTreeDeprec(classes_.size())),
    00773       columnCount_(columnCount)
    00774     {
    00775         for(unsigned int k=0; k<treeCount; ++k, ++trees, ++weights)
    00776         {
    00777             trees_[k].tree_ = *trees;
    00778             trees_[k].terminalWeights_ = *weights;
    00779         }
    00780     }
    00781
    00782     int featureCount() const
    00783     {
    00784         vigra_precondition(columnCount_ > 0,
    00785            "RandomForestDeprec::featureCount(): Random forest has not been trained yet.");
    00786         return columnCount_;
    00787     }
    00788
    00789     int labelCount() const
    00790     {
    00791         return classes_.size();
    00792     }
    00793
    00794     int treeCount() const
    00795     {
    00796         return trees_.size();
    00797     }
    00798
    00799     // loss == 0.0 means unweighted random forest
    00800     template <class U, class C, class Array, class Random>
    00801     double learn(MultiArrayView<2, U, C> const & features, Array const & labels,
    00802                Random const& random);
    00803
    00804     template <class U, class C, class Array>
    00805     double learn(MultiArrayView<2, U, C> const & features, Array const & labels)
    00806     {
    00807         RandomNumberGenerator<> generator(RandomSeed);
    00808         return learn(features, labels, generator);
    00809     }
    00810
    00811     template <class U, class C>
    00812     ClassLabelType predictLabel(MultiArrayView<2, U, C> const & features) const;
    00813
    00814     template <class U, class C1, class T, class C2>
    00815     void predictLabels(MultiArrayView<2, U, C1> const & features,
    00816                        MultiArrayView<2, T, C2> & labels) const
    00817     {
    00818         vigra_precondition(features.shape(0) == labels.shape(0),
    00819             "RandomForestDeprec::predictLabels(): Label array has wrong size.");
    00820         for(int k=0; k<features.shape(0); ++k)
    00821             labels(k,0) = predictLabel(rowVector(features, k));
    00822     }
    00823
    00824     template <class U, class C, class Iterator>
    00825     ClassLabelType predictLabel(MultiArrayView<2, U, C> const & features,
    00826                                 Iterator priors) const;
    00827
    00828     template <class U, class C1, class T, class C2>
    00829     void predictProbabilities(MultiArrayView<2, U, C1> const & features,
    00830                               MultiArrayView<2, T, C2> & prob) const;
    00831
    00832     template <class U, class C1, class T, class C2>
    00833     void predictNodes(MultiArrayView<2, U, C1> const & features,
    00834                                                    MultiArrayView<2, T, C2> & NodeIDs) const;
    00835 };
    00836
    00837 template <class ClassLabelType>
    00838 template <class U, class C1, class Array, class Random>
    00839 double
    00840 RandomForestDeprec<ClassLabelType>::learn(MultiArrayView<2, U, C1> const & features,
    00841                                              Array const & labels,
    00842                                              Random const& random)
    00843 {
    00844     unsigned int classCount = classes_.size();
    00845     unsigned int m = rowCount(features);
    00846     unsigned int n = columnCount(features);
    00847     vigra_precondition((unsigned int)(m) == (unsigned int)labels.size(),
    00848       "RandomForestDeprec::learn(): Label array has wrong size.");
    00849
    00850     vigra_precondition(options_.training_set_size <= m || options_.sample_with_replacement,
    00851        "RandomForestDeprec::learn(): Requested training set size exceeds total number of examples.");
    00852
    00853     MultiArrayIndex mtry = (options_.mtry == 0)
    00854                                 ? int(std::floor(std::sqrt(double(n)) + 0.5))
    00855                                 : options_.mtry;
    00856
    00857     vigra_precondition(mtry <= (MultiArrayIndex)n,
    00858        "RandomForestDeprec::learn(): mtry must be less than number of features.");
    00859
    00860     MultiArrayIndex msamples = options_.training_set_size;
    00861     if(options_.sample_classes_individually)
    00862         msamples = int(std::ceil(double(msamples) / classCount));
    00863
    00864     ArrayVector<int> intLabels(m), classExampleCounts(classCount);
    00865
    00866     // verify the input labels
    00867     int minClassCount;
    00868     {
    00869         typedef std::map<ClassLabelType, int > LabelChecker;
    00870         typedef typename LabelChecker::iterator LabelCheckerIterator;
    00871         LabelChecker labelChecker;
    00872         for(unsigned int k=0; k<classCount; ++k)
    00873             labelChecker[classes_[k]] = k;
    00874
    00875         for(unsigned int k=0; k<m; ++k)
    00876         {
    00877             LabelCheckerIterator found = labelChecker.find(labels[k]);
    00878             vigra_precondition(found != labelChecker.end(),
    00879                 "RandomForestDeprec::learn(): Unknown class label encountered.");
    00880             intLabels[k] = found->second;
    00881             ++classExampleCounts[intLabels[k]];
    00882         }
    00883         minClassCount = *argMin(classExampleCounts.begin(), classExampleCounts.end());
    00884         vigra_precondition(minClassCount > 0,
    00885              "RandomForestDeprec::learn(): At least one class is missing in the training set.");
    00886         if(msamples > 0 && options_.sample_classes_individually &&
    00887                           !options_.sample_with_replacement)
    00888         {
    00889             vigra_precondition(msamples <= minClassCount,
    00890                 "RandomForestDeprec::learn(): Too few examples in smallest class to reach "
    00891                 "requested training set size.");
    00892         }
    00893     }
    00894     columnCount_ = n;
    00895     ArrayVector<int> indices(m);
    00896     for(unsigned int k=0; k<m; ++k)
    00897         indices[k] = k;
    00898
    00899     if(options_.sample_classes_individually)
    00900     {
    00901         detail::RandomForestDeprecLabelSorter<ArrayVector<int> > sorter(intLabels);
    00902         std::sort(indices.begin(), indices.end(), sorter);
    00903     }
    00904
    00905     ArrayVector<int> usedIndices(m), oobCount(m), oobErrorCount(m);
    00906
    00907     UniformIntRandomFunctor<Random> randint(0, m-1, random);
    00908     //std::cerr << "Learning a RF \n";
    00909     for(unsigned int k=0; k<trees_.size(); ++k)
    00910     {
    00911        //std::cerr << "Learning tree " << k << " ...\n";
    00912
    00913         ArrayVector<int> trainingSet;
    00914         usedIndices.init(0);
    00915
    00916         if(options_.sample_classes_individually)
    00917         {
    00918             int first = 0;
    00919             for(unsigned int l=0; l<classCount; ++l)
    00920             {
    00921                 int lc = classExampleCounts[l];
    00922                 int lsamples = (msamples == 0)
    00923                                    ? int(std::ceil(options_.training_set_proportion*lc))
    00924                                    : msamples;
    00925
    00926                 if(options_.sample_with_replacement)
    00927                 {
    00928                     for(int ll=0; ll<lsamples; ++ll)
    00929                     {
    00930                         trainingSet.push_back(indices[first+randint(lc)]);
    00931                         ++usedIndices[trainingSet.back()];
    00932                     }
    00933                 }
    00934                 else
    00935                 {
    00936                     for(int ll=0; ll<lsamples; ++ll)
    00937                     {
    00938                         std::swap(indices[first+ll], indices[first+ll+randint(lc-ll)]);
    00939                         trainingSet.push_back(indices[first+ll]);
    00940                         ++usedIndices[trainingSet.back()];
    00941                     }
    00942                     //std::sort(indices.begin(), indices.begin()+lsamples);
    00943                 }
    00944                 first += lc;
    00945             }
    00946         }
    00947         else
    00948         {
    00949             if(msamples == 0)
    00950                 msamples = int(std::ceil(options_.training_set_proportion*m));
    00951
    00952             if(options_.sample_with_replacement)
    00953             {
    00954                 for(int l=0; l<msamples; ++l)
    00955                 {
    00956                     trainingSet.push_back(indices[randint(m)]);
    00957                     ++usedIndices[trainingSet.back()];
    00958                 }
    00959             }
    00960             else
    00961             {
    00962                 for(int l=0; l<msamples; ++l)
    00963                 {
    00964                     std::swap(indices[l], indices[l+randint(m-l)/*oikas*/]);
    00965                     trainingSet.push_back(indices[l]);
    00966                     ++usedIndices[trainingSet.back()];
    00967                 }
    00968
    00969
    00970             }
    00971
    00972         }
    00973         trees_[k].learn(features, intLabels,
    00974                         trainingSet.begin(), trainingSet.size(),
    00975                         options_.featuresPerNode(mtry), randint);
    00976 //        for(unsigned int l=0; l<m; ++l)
    00977 //        {
    00978 //            if(!usedIndices[l])
    00979 //            {
    00980 //                ++oobCount[l];
    00981 //                if(trees_[k].predictLabel(rowVector(features, l)) != intLabels[l])
    00982 //                    ++oobErrorCount[l];
    00983 //            }
    00984 //        }
    00985
    00986         for(unsigned int l=0; l<m; ++l)
    00987         {
    00988             if(!usedIndices[l])
    00989             {
    00990                 ++oobCount[l];
    00991                 if(trees_[k].predictLabel(rowVector(features, l)) != intLabels[l])
    00992                 {
    00993                     ++oobErrorCount[l];
    00994                     if(options_.oob_data.data() != 0)
    00995                         options_.oob_data(l, k) = 2;
    00996                 }
    00997                 else if(options_.oob_data.data() != 0)
    00998                 {
    00999                     options_.oob_data(l, k) = 1;
    01000                 }
    01001             }
    01002         }
    01003         // TODO: default value for oob_data
    01004         // TODO: implement variable importance
    01005         //if(!options_.sample_with_replacement){
    01006         //std::cerr << "done\n";
    01007         //trees_[k].print(std::cerr);
    01008         #ifdef VIGRA_RF_VERBOSE
    01009         trees_[k].printStatistics(std::cerr);
    01010         #endif
    01011     }
    01012     double oobError = 0.0;
    01013     int totalOobCount = 0;
    01014     for(unsigned int l=0; l<m; ++l)
    01015         if(oobCount[l])
    01016         {
    01017             oobError += double(oobErrorCount[l]) / oobCount[l];
    01018             ++totalOobCount;
    01019         }
    01020     return oobError / totalOobCount;
    01021 }
    01022
    01023 template <class ClassLabelType>
    01024 template <class U, class C>
    01025 ClassLabelType
    01026 RandomForestDeprec<ClassLabelType>::predictLabel(MultiArrayView<2, U, C> const & features) const
    01027 {
    01028     vigra_precondition(columnCount(features) >= featureCount(),
    01029         "RandomForestDeprec::predictLabel(): Too few columns in feature matrix.");
    01030     vigra_precondition(rowCount(features) == 1,
    01031         "RandomForestDeprec::predictLabel(): Feature matrix must have a single row.");
    01032     Matrix<double> prob(1, classes_.size());
    01033     predictProbabilities(features, prob);
    01034     return classes_[argMax(prob)];
    01035 }
    01036
    01037
    01038 //Same thing as above with priors for each label !!!
    01039 template <class ClassLabelType>
    01040 template <class U, class C, class Iterator>
    01041 ClassLabelType
    01042 RandomForestDeprec<ClassLabelType>::predictLabel(MultiArrayView<2, U, C> const & features,
    01043                                            Iterator priors) const
    01044 {
    01045     using namespace functor;
    01046     vigra_precondition(columnCount(features) >= featureCount(),
    01047         "RandomForestDeprec::predictLabel(): Too few columns in feature matrix.");
    01048     vigra_precondition(rowCount(features) == 1,
    01049         "RandomForestDeprec::predictLabel(): Feature matrix must have a single row.");
    01050     Matrix<double> prob(1,classes_.size());
    01051     predictProbabilities(features, prob);
    01052     std::transform(prob.begin(), prob.end(), priors, prob.begin(), Arg1()*Arg2());
    01053     return classes_[argMax(prob)];
    01054 }
    01055
    01056 template <class ClassLabelType>
    01057 template <class U, class C1, class T, class C2>
    01058 void
    01059 RandomForestDeprec<ClassLabelType>::predictProbabilities(MultiArrayView<2, U, C1> const & features,
    01060                                                    MultiArrayView<2, T, C2> & prob) const
    01061 {
    01062
    01063     //Features are n xp
    01064     //prob is n x NumOfLabel probability for each feature in each class
    01065
    01066     vigra_precondition(rowCount(features) == rowCount(prob),
    01067       "RandomForestDeprec::predictProbabilities(): Feature matrix and probability matrix size mismatch.");
    01068
    01069     // num of features must be bigger than num of features in Random forest training
    01070     // but why bigger?
    01071     vigra_precondition(columnCount(features) >= featureCount(),
    01072       "RandomForestDeprec::predictProbabilities(): Too few columns in feature matrix.");
    01073     vigra_precondition(columnCount(prob) == (MultiArrayIndex)labelCount(),
    01074       "RandomForestDeprec::predictProbabilities(): Probability matrix must have as many columns as there are classes.");
    01075
    01076     //Classify for each row.
    01077     for(int row=0; row < rowCount(features); ++row)
    01078     {
    01079     //contains the weights returned by a single tree???
    01080     //thought that one tree has only one vote???
    01081     //Pruning???
    01082         ArrayVector<double>::const_iterator weights;
    01083
    01084         //totalWeight == totalVoteCount!
    01085     double totalWeight = 0.0;
    01086
    01087     //Set each VoteCount = 0 - prob(row,l) contains vote counts until
    01088     //further normalisation
    01089         for(unsigned int l=0; l<classes_.size(); ++l)
    01090             prob(row, l) = 0.0;
    01091
    01092     //Let each tree classify...
    01093         for(unsigned int k=0; k<trees_.size(); ++k)
    01094         {
    01095         //get weights predicted by single tree
    01096             weights = trees_[k].predict(rowVector(features, row));
    01097
    01098         //update votecount.
    01099             for(unsigned int l=0; l<classes_.size(); ++l)
    01100             {
    01101                 prob(row, l) += detail::RequiresExplicitCast<T>::cast(weights[l]);
    01102                 //every weight in totalWeight.
    01103                 totalWeight += weights[l];
    01104             }
    01105         }
    01106
    01107     //Normalise votes in each row by total VoteCount (totalWeight
    01108         for(unsigned int l=0; l<classes_.size(); ++l)
    01109                 prob(row, l) /= detail::RequiresExplicitCast<T>::cast(totalWeight);
    01110     }
    01111 }
    01112
    01113
    01114 template <class ClassLabelType>
    01115 template <class U, class C1, class T, class C2>
    01116 void
    01117 RandomForestDeprec<ClassLabelType>::predictNodes(MultiArrayView<2, U, C1> const & features,
    01118                                                    MultiArrayView<2, T, C2> & NodeIDs) const
    01119 {
    01120     vigra_precondition(columnCount(features) >= featureCount(),
    01121       "RandomForestDeprec::getNodesRF(): Too few columns in feature matrix.");
    01122     vigra_precondition(rowCount(features) <= rowCount(NodeIDs),
    01123       "RandomForestDeprec::getNodesRF(): Too few rows in NodeIds matrix");
    01124     vigra_precondition(columnCount(NodeIDs) >= treeCount(),
    01125       "RandomForestDeprec::getNodesRF(): Too few columns in NodeIds matrix.");
    01126     NodeIDs.init(0);
    01127     for(unsigned int k=0; k<trees_.size(); ++k)
    01128     {
    01129         for(int row=0; row < rowCount(features); ++row)
    01130         {
    01131             NodeIDs(row,k) = trees_[k].leafID(rowVector(features, row));
    01132         }
    01133     }
    01134 }
    01135
    01136 //@}
    01137
    01138 } // namespace vigra
    01139
    01140
    01141 #endif // VIGRA_RANDOM_FOREST_HXX
    01142

© 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)