MethodDT.cxx

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// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : MethodDT (DT = Decision Trees)                                         *
 *                                             *
 *                                                                                *
 * Description:                                                                   *
 *      Analysis of Boosted Decision Trees                                        *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland              *
 *      Helge Voss      <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany      *
 *      Or Cohen        <orcohenor@gmail.com>    - Weizmann Inst., Israel         *
 *                                                                                *
 * Copyright (c) 2005:                                                            *
 *      CERN, Switzerland                                                         *
 *      MPI-K Heidelberg, Germany                                                 *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (see tmva/doc/LICENSE)                                          *
 **********************************************************************************/
 
/*! \class TMVA::MethodDT
\ingroup TMVA
 
Analysis of Boosted Decision Trees
 
Boosted decision trees have been successfully used in High Energy
Physics analysis for example by the MiniBooNE experiment
(Yang-Roe-Zhu, physics/0508045). In Boosted Decision Trees, the
selection is done on a majority vote on the result of several decision
trees, which are all derived from the same training sample by
supplying different event weights during the training.
 
### Decision trees:
 
successive decision nodes are used to categorize the
events out of the sample as either signal or background. Each node
uses only a single discriminating variable to decide if the event is
signal-like ("goes right") or background-like ("goes left"). This
forms a tree like structure with "baskets" at the end (leave nodes),
and an event is classified as either signal or background according to
whether the basket where it ends up has been classified signal or
background during the training. Training of a decision tree is the
process to define the "cut criteria" for each node. The training
starts with the root node. Here one takes the full training event
sample and selects the variable and corresponding cut value that gives
the best separation between signal and background at this stage. Using
this cut criterion, the sample is then divided into two subsamples, a
signal-like (right) and a background-like (left) sample. Two new nodes
are then created for each of the two sub-samples and they are
constructed using the same mechanism as described for the root
node. The devision is stopped once a certain node has reached either a
minimum number of events, or a minimum or maximum signal purity. These
leave nodes are then called "signal" or "background" if they contain
more signal respective background events from the training sample.
 
### Boosting:
 
the idea behind the boosting is, that signal events from the training
sample, that *end up in a background node (and vice versa) are given a
larger weight than events that are in the correct leave node. This
results in a re-weighed training event sample, with which then a new
decision tree can be developed. The boosting can be applied several
times (typically 100-500 times) and one ends up with a set of decision
trees (a forest).
 
### Bagging:
 
In this particular variant of the Boosted Decision Trees the boosting
is not done on the basis of previous training results, but by a simple
stochastic re-sampling of the initial training event sample.
 
### Analysis:
 
applying an individual decision tree to a test event results in a
classification of the event as either signal or background. For the
boosted decision tree selection, an event is successively subjected to
the whole set of decision trees and depending on how often it is
classified as signal, a "likelihood" estimator is constructed for the
event being signal or background. The value of this estimator is the
one which is then used to select the events from an event sample, and
the cut value on this estimator defines the efficiency and purity of
the selection.
*/
 
#include "TMVA/MethodDT.h"
 
#include "TMVA/BinarySearchTree.h"
#include "TMVA/CCPruner.h"
#include "TMVA/ClassifierFactory.h"
#include "TMVA/Configurable.h"
#include "TMVA/CrossEntropy.h"
#include "TMVA/DataSet.h"
#include "TMVA/DecisionTree.h"
#include "TMVA/GiniIndex.h"
#include "TMVA/IMethod.h"
#include "TMVA/MethodBase.h"
#include "TMVA/MethodBoost.h"
#include "TMVA/MisClassificationError.h"
#include "TMVA/MsgLogger.h"
#include "TMVA/Ranking.h"
#include "TMVA/SdivSqrtSplusB.h"
#include "TMVA/SeparationBase.h"
#include "TMVA/Timer.h"
#include "TMVA/Tools.h"
#include "TMVA/Types.h"
 
#include "TRandom3.h"
 
#include <iostream>
#include <algorithm>
 
using std::vector;
 
REGISTER_METHOD(DT)
 
 
////////////////////////////////////////////////////////////////////////////////
/// the standard constructor for just an ordinar "decision trees"
 
   TMVA::MethodDT::MethodDT( const TString& jobName,
                             const TString& methodTitle,
                             DataSetInfo& theData,
                             const TString& theOption) :
   TMVA::MethodBase( jobName, Types::kDT, methodTitle, theData, theOption)
   , fTree(0)
   , fSepType(0)
   , fMinNodeEvents(0)
   , fMinNodeSize(0)
   , fNCuts(0)
   , fUseYesNoLeaf(kFALSE)
   , fNodePurityLimit(0)
   , fMaxDepth(0)
   , fErrorFraction(0)
   , fPruneStrength(0)
   , fPruneMethod(DecisionTree::kNoPruning)
   , fAutomatic(kFALSE)
   , fRandomisedTrees(kFALSE)
   , fUseNvars(0)
   , fUsePoissonNvars(0)  // don't use this initialisation, only here to make  Coverity happy. Is set in Init()
   , fDeltaPruneStrength(0)
{
      fPruneBeforeBoost = kFALSE;
}
 
////////////////////////////////////////////////////////////////////////////////
///constructor from Reader
 
TMVA::MethodDT::MethodDT( DataSetInfo& dsi,
                          const TString& theWeightFile) :
   TMVA::MethodBase( Types::kDT, dsi, theWeightFile)
   , fTree(0)
   , fSepType(0)
   , fMinNodeEvents(0)
   , fMinNodeSize(0)
   , fNCuts(0)
   , fUseYesNoLeaf(kFALSE)
   , fNodePurityLimit(0)
   , fMaxDepth(0)
   , fErrorFraction(0)
   , fPruneStrength(0)
   , fPruneMethod(DecisionTree::kNoPruning)
   , fAutomatic(kFALSE)
   , fRandomisedTrees(kFALSE)
   , fUseNvars(0)
   , fDeltaPruneStrength(0)
{
      fPruneBeforeBoost = kFALSE;
}
 
////////////////////////////////////////////////////////////////////////////////
/// FDA can handle classification with 2 classes and regression with one regression-target
 
Bool_t TMVA::MethodDT::HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t /*numberTargets*/ )
{
   if( type == Types::kClassification && numberClasses == 2 ) return kTRUE;
   return kFALSE;
}
 
 
////////////////////////////////////////////////////////////////////////////////
/// Define the options (their key words) that can be set in the option string.
///
///  - UseRandomisedTrees  choose at each node splitting a random set of variables
///  - UseNvars         use UseNvars variables in randomised trees
///  - SeparationType   the separation criterion applied in the node splitting.
///                    known:
///                          - GiniIndex
///                          - MisClassificationError
///                          - CrossEntropy
///                          - SDivSqrtSPlusB
///  - nEventsMin:      the minimum number of events in a node (leaf criteria, stop splitting)
///  - nCuts:           the number of steps in the optimisation of the cut for a node (if < 0, then
///                    step size is determined by the events)
///  - UseYesNoLeaf     decide if the classification is done simply by the node type, or the S/B
///                    (from the training) in the leaf node
///  - NodePurityLimit  the minimum purity to classify a node as a signal node (used in pruning and boosting to determine
///                    misclassification error rate)
///  - PruneMethod      The Pruning method:
///                    known:
///                          - NoPruning  // switch off pruning completely
///                          - ExpectedError
///                          - CostComplexity
///  - PruneStrength    a parameter to adjust the amount of pruning. Should be large enough such that overtraining is avoided");
 
void TMVA::MethodDT::DeclareOptions()
{
   DeclareOptionRef(fRandomisedTrees,"UseRandomisedTrees","Choose at each node splitting a random set of variables and *bagging*");
   DeclareOptionRef(fUseNvars,"UseNvars","Number of variables used if randomised Tree option is chosen");
   DeclareOptionRef(fUsePoissonNvars,"UsePoissonNvars", "Interpret \"UseNvars\" not as fixed number but as mean of a Poisson distribution in each split with RandomisedTree option");
   DeclareOptionRef(fUseYesNoLeaf=kTRUE, "UseYesNoLeaf",
                    "Use Sig or Bkg node type or the ratio S/B as classification in the leaf node");
   DeclareOptionRef(fNodePurityLimit=0.5, "NodePurityLimit", "In boosting/pruning, nodes with purity > NodePurityLimit are signal; background otherwise.");
   DeclareOptionRef(fSepTypeS="GiniIndex", "SeparationType", "Separation criterion for node splitting");
   AddPreDefVal(TString("MisClassificationError"));
   AddPreDefVal(TString("GiniIndex"));
   AddPreDefVal(TString("CrossEntropy"));
   AddPreDefVal(TString("SDivSqrtSPlusB"));
   DeclareOptionRef(fMinNodeEvents=-1, "nEventsMin", "deprecated !!! Minimum number of events required in a leaf node");
   DeclareOptionRef(fMinNodeSizeS, "MinNodeSize", "Minimum percentage of training events required in a leaf node (default: Classification: 10%, Regression: 1%)");
   DeclareOptionRef(fNCuts, "nCuts", "Number of steps during node cut optimisation");
   DeclareOptionRef(fPruneStrength, "PruneStrength", "Pruning strength (negative value == automatic adjustment)");
   DeclareOptionRef(fPruneMethodS="NoPruning", "PruneMethod", "Pruning method: NoPruning (switched off), ExpectedError or CostComplexity");
 
   AddPreDefVal(TString("NoPruning"));
   AddPreDefVal(TString("ExpectedError"));
   AddPreDefVal(TString("CostComplexity"));
 
   if (DoRegression()) {
      DeclareOptionRef(fMaxDepth=50,"MaxDepth","Max depth of the decision tree allowed");
   }else{
      DeclareOptionRef(fMaxDepth=3,"MaxDepth","Max depth of the decision tree allowed");
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// options that are used ONLY for the READER to ensure backward compatibility
 
void TMVA::MethodDT::DeclareCompatibilityOptions() {
 
   MethodBase::DeclareCompatibilityOptions();
 
   DeclareOptionRef(fPruneBeforeBoost=kFALSE, "PruneBeforeBoost",
                    "--> removed option .. only kept for reader backward compatibility");
}
 
////////////////////////////////////////////////////////////////////////////////
/// the option string is decoded, for available options see "DeclareOptions"
 
void TMVA::MethodDT::ProcessOptions()
{
   fSepTypeS.ToLower();
   if      (fSepTypeS == "misclassificationerror") fSepType = new MisClassificationError();
   else if (fSepTypeS == "giniindex")              fSepType = new GiniIndex();
   else if (fSepTypeS == "crossentropy")           fSepType = new CrossEntropy();
   else if (fSepTypeS == "sdivsqrtsplusb")         fSepType = new SdivSqrtSplusB();
   else {
      Log() << kINFO << GetOptions() << Endl;
      Log() << kFATAL << "<ProcessOptions> unknown Separation Index option called" << Endl;
   }
 
   //   std::cout << "fSeptypes " << fSepTypeS << "  fseptype " << fSepType << std::endl;
 
   fPruneMethodS.ToLower();
   if      (fPruneMethodS == "expectederror" )   fPruneMethod = DecisionTree::kExpectedErrorPruning;
   else if (fPruneMethodS == "costcomplexity" )  fPruneMethod = DecisionTree::kCostComplexityPruning;
   else if (fPruneMethodS == "nopruning" )       fPruneMethod = DecisionTree::kNoPruning;
   else {
      Log() << kINFO << GetOptions() << Endl;
      Log() << kFATAL << "<ProcessOptions> unknown PruneMethod option:" << fPruneMethodS <<" called" << Endl;
   }
 
   if (fPruneStrength < 0) fAutomatic = kTRUE;
   else fAutomatic = kFALSE;
   if (fAutomatic && fPruneMethod == DecisionTree::kExpectedErrorPruning){
      Log() << kFATAL
            <<  "Sorry automatic pruning strength determination is not implemented yet for ExpectedErrorPruning" << Endl;
   }
 
 
   if (this->Data()->HasNegativeEventWeights()){
      Log() << kINFO << " You are using a Monte Carlo that has also negative weights. "
            << "That should in principle be fine as long as on average you end up with "
            << "something positive. For this you have to make sure that the minimal number "
            << "of (un-weighted) events demanded for a tree node (currently you use: MinNodeSize="
            <<fMinNodeSizeS
            <<", (or the deprecated equivalent nEventsMin) you can set this via the "
            <<"MethodDT option string when booking the "
            << "classifier) is large enough to allow for reasonable averaging!!! "
            << " If this does not help.. maybe you want to try the option: IgnoreNegWeightsInTraining  "
            << "which ignores events with negative weight in the training. " << Endl
            << Endl << "Note: You'll get a WARNING message during the training if that should ever happen" << Endl;
   }
 
   if (fRandomisedTrees){
      Log() << kINFO << " Randomised trees should use *bagging* as *boost* method. Did you set this in the *MethodBoost* ? . Here I can enforce only the *no pruning*" << Endl;
      fPruneMethod = DecisionTree::kNoPruning;
      //      fBoostType   = "Bagging";
   }
 
   if (fMinNodeEvents > 0){
      fMinNodeSize = fMinNodeEvents / Data()->GetNTrainingEvents() * 100;
      Log() << kWARNING << "You have explicitly set *nEventsMin*, the min absolute number \n"
            << "of events in a leaf node. This is DEPRECATED, please use the option \n"
            << "*MinNodeSize* giving the relative number as percentage of training \n"
            << "events instead. \n"
            << "nEventsMin="<<fMinNodeEvents<< "--> MinNodeSize="<<fMinNodeSize<<"%"
            << Endl;
   }else{
      SetMinNodeSize(fMinNodeSizeS);
   }
}
 
void TMVA::MethodDT::SetMinNodeSize(Double_t sizeInPercent){
   if (sizeInPercent > 0 && sizeInPercent < 50){
      fMinNodeSize=sizeInPercent;
 
   } else {
      Log() << kERROR << "you have demanded a minimal node size of "
            << sizeInPercent << "% of the training events.. \n"
            << " that somehow does not make sense "<<Endl;
   }
 
}
void TMVA::MethodDT::SetMinNodeSize(TString sizeInPercent){
   sizeInPercent.ReplaceAll("%","");
   if (sizeInPercent.IsAlnum()) SetMinNodeSize(sizeInPercent.Atof());
   else {
      Log() << kERROR << "I had problems reading the option MinNodeEvents, which\n"
            << "after removing a possible % sign now reads " << sizeInPercent << Endl;
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// common initialisation with defaults for the DT-Method
 
void TMVA::MethodDT::Init( void )
{
   fMinNodeEvents  = -1;
   fMinNodeSize    = 5;
   fMinNodeSizeS   = "5%";
   fNCuts          = 20;
   fPruneMethod    = DecisionTree::kNoPruning;
   fPruneStrength  = 5;     // -1 means automatic determination of the prune strength using a validation sample
   fDeltaPruneStrength=0.1;
   fRandomisedTrees= kFALSE;
   fUseNvars       = GetNvar();
   fUsePoissonNvars = kTRUE;
 
   // reference cut value to distinguish signal-like from background-like events
   SetSignalReferenceCut( 0 );
   if (fAnalysisType == Types::kClassification || fAnalysisType == Types::kMulticlass ) {
      fMaxDepth        = 3;
   }else {
      fMaxDepth = 50;
   }
}
 
////////////////////////////////////////////////////////////////////////////////
///destructor
 
TMVA::MethodDT::~MethodDT( void )
{
   delete fTree;
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::MethodDT::Train( void )
{
   TMVA::DecisionTreeNode::SetIsTraining(true);
   fTree = new DecisionTree( fSepType, fMinNodeSize, fNCuts, &(DataInfo()), 0,
                             fRandomisedTrees, fUseNvars, fUsePoissonNvars,fMaxDepth,0 );
   fTree->SetNVars(GetNvar());
   if (fRandomisedTrees) Log()<<kWARNING<<" randomised Trees do not work yet in this framework,"
                              << " as I do not know how to give each tree a new random seed, now they"
                              << " will be all the same and that is not good " << Endl;
   fTree->SetAnalysisType( GetAnalysisType() );
 
   //fTree->BuildTree(GetEventCollection(Types::kTraining));
   Data()->SetCurrentType(Types::kTraining);
   UInt_t nevents = Data()->GetNTrainingEvents();
   std::vector<const TMVA::Event*> tmp;
   for (Long64_t ievt=0; ievt<nevents; ievt++) {
      const Event *event = GetEvent(ievt);
      tmp.push_back(event);
   }
   fTree->BuildTree(tmp);
   if (fPruneMethod != DecisionTree::kNoPruning) fTree->PruneTree();
 
   TMVA::DecisionTreeNode::SetIsTraining(false);
   ExitFromTraining();
}
 
////////////////////////////////////////////////////////////////////////////////
/// prune the decision tree if requested (good for individual trees that are best grown out, and then
/// pruned back, while boosted decision trees are best 'small' trees to start with. Well, at least the
/// standard "optimal pruning algorithms" don't result in 'weak enough' classifiers !!
 
Double_t TMVA::MethodDT::PruneTree( )
{
   // remember the number of nodes beforehand (for monitoring purposes)
 
 
   if (fAutomatic && fPruneMethod == DecisionTree::kCostComplexityPruning) { // automatic cost complexity pruning
      CCPruner* pruneTool = new CCPruner(fTree, this->Data() , fSepType);
      pruneTool->Optimize();
      std::vector<DecisionTreeNode*> nodes = pruneTool->GetOptimalPruneSequence();
      fPruneStrength = pruneTool->GetOptimalPruneStrength();
      for(UInt_t i = 0; i < nodes.size(); i++)
         fTree->PruneNode(nodes[i]);
      delete pruneTool;
   }
   else if (fAutomatic &&  fPruneMethod != DecisionTree::kCostComplexityPruning){
      /*
 
        Double_t alpha = 0;
        Double_t delta = fDeltaPruneStrength;
 
        DecisionTree*  dcopy;
        std::vector<Double_t> q;
        multimap<Double_t,Double_t> quality;
        Int_t nnodes=fTree->GetNNodes();
 
        // find the maximum prune strength that still leaves some nodes
        Bool_t forceStop = kFALSE;
        Int_t troubleCount=0, previousNnodes=nnodes;
 
 
        nnodes=fTree->GetNNodes();
        while (nnodes > 3 && !forceStop) {
        dcopy = new DecisionTree(*fTree);
        dcopy->SetPruneStrength(alpha+=delta);
        dcopy->PruneTree();
        q.push_back(TestTreeQuality(dcopy));
        quality.insert(std::pair<const Double_t,Double_t>(q.back(),alpha));
        nnodes=dcopy->GetNNodes();
        if (previousNnodes == nnodes) troubleCount++;
        else {
        troubleCount=0; // reset counter
        if (nnodes < previousNnodes / 2 ) fDeltaPruneStrength /= 2.;
        }
        previousNnodes = nnodes;
        if (troubleCount > 20) {
        if (methodIndex == 0 && fPruneStrength <=0) {//maybe you need larger stepsize ??
        fDeltaPruneStrength *= 5;
        Log() << kINFO << "<PruneTree> trouble determining optimal prune strength"
        << " for Tree " << methodIndex
        << " --> first try to increase the step size"
        << " currently Prunestrenght= " << alpha
        << " stepsize " << fDeltaPruneStrength << " " << Endl;
        troubleCount = 0;   // try again
        fPruneStrength = 1; // if it was for the first time..
        } else if (methodIndex == 0 && fPruneStrength <=2) {//maybe you need much larger stepsize ??
        fDeltaPruneStrength *= 5;
        Log() << kINFO << "<PruneTree> trouble determining optimal prune strength"
        << " for Tree " << methodIndex
        << " -->  try to increase the step size even more.. "
        << " if that still didn't work, TRY IT BY HAND"
        << " currently Prunestrenght= " << alpha
        << " stepsize " << fDeltaPruneStrength << " " << Endl;
        troubleCount = 0;   // try again
        fPruneStrength = 3; // if it was for the first time..
        } else {
        forceStop=kTRUE;
        Log() << kINFO << "<PruneTree> trouble determining optimal prune strength"
        << " for Tree " << methodIndex << " at tested prune strength: " << alpha << " --> abort forced, use same strength as for previous tree:"
        << fPruneStrength << Endl;
        }
        }
        if (fgDebugLevel==1) Log() << kINFO << "Pruneed with ("<<alpha
        << ") give quality: " << q.back()
        << " and #nodes: " << nnodes
        << Endl;
        delete dcopy;
        }
        if (!forceStop) {
        multimap<Double_t,Double_t>::reverse_iterator it=quality.rend();
        it++;
        fPruneStrength = it->second;
        // adjust the step size for the next tree.. think that 20 steps are sort of
        // fine enough.. could become a tunable option later..
        fDeltaPruneStrength *= Double_t(q.size())/20.;
        }
 
        fTree->SetPruneStrength(fPruneStrength);
        fTree->PruneTree();
      */
   }
   else {
      fTree->SetPruneStrength(fPruneStrength);
      fTree->PruneTree();
   }
 
   return fPruneStrength;
}
 
////////////////////////////////////////////////////////////////////////////////
 
Double_t TMVA::MethodDT::TestTreeQuality( DecisionTree *dt )
{
   Data()->SetCurrentType(Types::kValidation);
   // test the tree quality.. in terms of Misclassification
   Double_t SumCorrect=0,SumWrong=0;
   for (Long64_t ievt=0; ievt<Data()->GetNEvents(); ievt++)
      {
         const Event * ev = Data()->GetEvent(ievt);
         if ((dt->CheckEvent(ev) > dt->GetNodePurityLimit() ) == DataInfo().IsSignal(ev)) SumCorrect+=ev->GetWeight();
         else SumWrong+=ev->GetWeight();
      }
   Data()->SetCurrentType(Types::kTraining);
   return  SumCorrect / (SumCorrect + SumWrong);
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::MethodDT::AddWeightsXMLTo( void* parent ) const
{
   fTree->AddXMLTo(parent);
   //Log() << kFATAL << "Please implement writing of weights as XML" << Endl;
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::MethodDT::ReadWeightsFromXML( void* wghtnode)
{
   if(fTree)
      delete fTree;
   fTree = new DecisionTree();
   fTree->ReadXML(wghtnode,GetTrainingTMVAVersionCode());
}
 
////////////////////////////////////////////////////////////////////////////////
 
void  TMVA::MethodDT::ReadWeightsFromStream( std::istream& istr )
{
   delete fTree;
   fTree = new DecisionTree();
   fTree->Read(istr);
}
 
////////////////////////////////////////////////////////////////////////////////
/// returns MVA value
 
Double_t TMVA::MethodDT::GetMvaValue( Double_t* err, Double_t* errUpper )
{
   // cannot determine error
   NoErrorCalc(err, errUpper);
 
   return fTree->CheckEvent(GetEvent(),fUseYesNoLeaf);
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::MethodDT::GetHelpMessage() const
{
}
////////////////////////////////////////////////////////////////////////////////
 
const TMVA::Ranking* TMVA::MethodDT::CreateRanking()
{
   return 0;
}