| 1 | /* ======================================================================== *\
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| 2 | ! $Name: not supported by cvs2svn $:$Id: MRanForestCalc.cc,v 1.30 2007-08-24 12:58:49 tbretz Exp $
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| 3 | ! --------------------------------------------------------------------------
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| 4 | !
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| 5 | ! *
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| 6 | ! * This file is part of MARS, the MAGIC Analysis and Reconstruction
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| 7 | ! * Software. It is distributed to you in the hope that it can be a useful
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| 8 | ! * and timesaving tool in analysing Data of imaging Cerenkov telescopes.
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| 9 | ! * It is distributed WITHOUT ANY WARRANTY.
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| 10 | ! *
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| 11 | ! * Permission to use, copy, modify and distribute this software and its
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| 12 | ! * documentation for any purpose is hereby granted without fee,
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| 13 | ! * provided that the above copyright notice appear in all copies and
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| 14 | ! * that both that copyright notice and this permission notice appear
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| 15 | ! * in supporting documentation. It is provided "as is" without express
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| 16 | ! * or implied warranty.
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| 17 | ! *
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| 18 | !
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| 19 | !
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| 20 | ! Author(s): Thomas Hengstebeck 2/2005 <mailto:hengsteb@physik.hu-berlin.de>
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| 21 | ! Author(s): Thomas Bretz 8/2005 <mailto:tbretz@astro.uni-wuerzburg.de>
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| 22 | !
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| 23 | ! Copyright: MAGIC Software Development, 2000-2006
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| 24 | !
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| 25 | !
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| 26 | \* ======================================================================== */
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| 27 |
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| 28 | /////////////////////////////////////////////////////////////////////////////
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| 29 | //
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| 30 | // MRanForestCalc
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| 31 | //
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| 32 | //
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| 33 | ////////////////////////////////////////////////////////////////////////////
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| 34 | #include "MRanForestCalc.h"
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| 35 |
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| 36 | #include <TF1.h>
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| 37 | #include <TFile.h>
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| 38 | #include <TGraph.h>
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| 39 | #include <TVector.h>
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| 40 |
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| 41 | #include "MHMatrix.h"
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| 42 |
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| 43 | #include "MLog.h"
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| 44 | #include "MLogManip.h"
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| 45 |
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| 46 | #include "MData.h"
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| 47 | #include "MDataArray.h"
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| 48 |
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| 49 | #include "MRanForest.h"
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| 50 | #include "MParameters.h"
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| 51 |
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| 52 | #include "MParList.h"
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| 53 | #include "MTaskList.h"
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| 54 | #include "MEvtLoop.h"
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| 55 | #include "MRanForestGrow.h"
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| 56 | #include "MFillH.h"
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| 57 |
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| 58 | ClassImp(MRanForestCalc);
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| 59 |
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| 60 | using namespace std;
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| 61 |
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| 62 | const TString MRanForestCalc::gsDefName = "MRanForestCalc";
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| 63 | const TString MRanForestCalc::gsDefTitle = "RF for energy estimation";
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| 64 |
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| 65 | const TString MRanForestCalc::gsNameOutput = "RanForestOut";
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| 66 | const TString MRanForestCalc::gsNameEvalFunc = "EvalFunction";
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| 67 |
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| 68 | MRanForestCalc::MRanForestCalc(const char *name, const char *title)
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| 69 | : fData(0), fRFOut(0), fTestMatrix(0), fFunc("x"),
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| 70 | fNumTrees(-1), fNumTry(-1), fNdSize(-1), fNumObsoleteVariables(1),
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| 71 | fLastDataColumnHasWeights(kFALSE),
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| 72 | fNameOutput(gsNameOutput), fDebug(kFALSE), fEstimationMode(kMean)
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| 73 | {
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| 74 | fName = name ? name : gsDefName.Data();
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| 75 | fTitle = title ? title : gsDefTitle.Data();
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| 76 |
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| 77 | // FIXME:
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| 78 | fNumTrees = 100; //100
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| 79 | fNumTry = 0; //3 0 means: in MRanForest estimated best value will be calculated
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| 80 | fNdSize = 1; //1
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| 81 | }
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| 82 |
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| 83 | MRanForestCalc::~MRanForestCalc()
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| 84 | {
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| 85 | fEForests.Delete();
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| 86 | }
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| 87 |
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| 88 | // --------------------------------------------------------------------------
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| 89 | //
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| 90 | // Set a function which is applied to the output of the random forest
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| 91 | //
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| 92 | Bool_t MRanForestCalc::SetFunction(const char *func)
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| 93 | {
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| 94 | return !fFunc.SetRule(func);
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| 95 | }
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| 96 |
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| 97 | // --------------------------------------------------------------------------
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| 98 | //
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| 99 | // ver=0: One yes/no-classification forest is trained for each bin.
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| 100 | // the yes/no classification is done using the grid
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| 101 | // ver=1: One classification forest is trained. The last column contains a
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| 102 | // value which is turned into a classifier by rf itself using the grid
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| 103 | // ver=2: One classification forest is trained. The last column already contains
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| 104 | // the classifier
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| 105 | // ver=3: A regression forest is trained. The last column contains the
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| 106 | // classifier
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| 107 | //
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| 108 | Int_t MRanForestCalc::Train(const MHMatrix &matrixtrain, const TArrayD &grid, Int_t ver)
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| 109 | {
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| 110 | gLog.Separator("MRanForestCalc - Train");
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| 111 |
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| 112 | if (!matrixtrain.GetColumns())
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| 113 | {
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| 114 | *fLog << err << "ERROR - MHMatrix does not contain rules... abort." << endl;
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| 115 | return kFALSE;
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| 116 | }
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| 117 |
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| 118 | const Int_t ncols = matrixtrain.GetM().GetNcols();
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| 119 | const Int_t nrows = matrixtrain.GetM().GetNrows();
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| 120 | if (ncols<=0 || nrows <=0)
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| 121 | {
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| 122 | *fLog << err << "ERROR - No. of columns or no. of rows of matrixtrain equal 0 ... abort." << endl;
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| 123 | return kFALSE;
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| 124 | }
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| 125 |
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| 126 | // rules (= combination of image par) to be used for energy estimation
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| 127 | TFile fileRF(fFileName, "recreate");
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| 128 | if (!fileRF.IsOpen())
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| 129 | {
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| 130 | *fLog << err << "ERROR - File to store RFs could not be opened... abort." << endl;
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| 131 | return kFALSE;
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| 132 | }
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| 133 |
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| 134 | // The number of columns which have to be removed for the training
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| 135 | // The last data column may contain weight which also have to be removed
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| 136 | const Int_t nobs = fNumObsoleteVariables + (fLastDataColumnHasWeights?1:0); // Number of obsolete columns
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| 137 |
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| 138 | const MDataArray &dcol = *matrixtrain.GetColumns();
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| 139 |
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| 140 | // Make a copy of the rules for accessing the train-data
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| 141 | MDataArray usedrules;
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| 142 | for (Int_t i=0; i<ncols; i++)
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| 143 | if (i<ncols-nobs) // -3 is important!!!
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| 144 | usedrules.AddEntry(dcol[i].GetRule());
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| 145 | else
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| 146 | *fLog << inf << "Skipping " << dcol[i].GetRule() << " for training" << endl;
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| 147 |
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| 148 | // In the case of regression store the rule to be regessed in the
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| 149 | // last entry of your rules
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| 150 | MDataArray rules(usedrules);
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| 151 | rules.AddEntry(ver<3?"Classification.fVal":dcol[ncols-1].GetRule().Data());
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| 152 |
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| 153 | // prepare train-matrix finally used
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| 154 | TMatrix mat(matrixtrain.GetM());
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| 155 |
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| 156 | // Resize it such that the obsolete columns are removed
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| 157 | mat.ResizeTo(nrows, ncols-nobs+1);
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| 158 |
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| 159 | if (fDebug)
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| 160 | gLog.SetNullOutput(kTRUE);
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| 161 |
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| 162 | // In the case one independant RF is trained for each bin (e.g.
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| 163 | // energy-bin) train all of them
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| 164 | const Int_t nbins = ver>0 ? 1 : grid.GetSize()-1;
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| 165 | for (Int_t ie=0; ie<nbins; ie++)
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| 166 | {
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| 167 | // In the case weights should be used initialize the
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| 168 | // corresponding array
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| 169 | Double_t sum = 0;
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| 170 |
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| 171 | TArrayF weights(nrows);
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| 172 | if (fLastDataColumnHasWeights)
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| 173 | {
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| 174 | for (Int_t j=0; j<nrows; j++)
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| 175 | {
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| 176 | weights[j] = matrixtrain.GetM()(j, ncols-nobs);
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| 177 | sum += weights[j];
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| 178 | }
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| 179 | }
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| 180 |
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| 181 | *fLog << inf << "MRanForestCalc::Train: Sum of weights " << sum << endl;
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| 182 |
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| 183 | // Setup the matrix such that the last comlumn contains
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| 184 | // the classifier or the regeression target value
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| 185 | switch (ver)
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| 186 | {
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| 187 | case 0: // Replace last column by a classification which is 1 in
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| 188 | // the case the event belongs to this bin, 0 otherwise
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| 189 | {
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| 190 | Int_t irows=0;
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| 191 | for (Int_t j=0; j<nrows; j++)
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| 192 | {
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| 193 | const Double_t value = matrixtrain.GetM()(j,ncols-1);
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| 194 | const Bool_t inside = value>grid[ie] && value<=grid[ie+1];
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| 195 |
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| 196 | mat(j, ncols-nobs) = inside ? 1 : 0;
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| 197 |
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| 198 | if (inside)
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| 199 | irows++;
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| 200 | }
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| 201 | if (irows==0)
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| 202 | *fLog << warn << "WARNING - Skipping";
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| 203 | else
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| 204 | *fLog << inf << "Training RF for";
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| 205 |
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| 206 | *fLog << " bin " << ie << " (" << grid[ie] << ", " << grid[ie+1] << ") " << irows << "/" << nrows << endl;
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| 207 |
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| 208 | if (irows==0)
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| 209 | continue;
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| 210 | }
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| 211 | break;
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| 212 |
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| 213 | case 1: // Use last column as classifier or for regression
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| 214 | case 2:
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| 215 | case 3:
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| 216 | for (Int_t j=0; j<nrows; j++)
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| 217 | mat(j, ncols-nobs) = matrixtrain.GetM()(j,ncols-1);
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| 218 | break;
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| 219 | }
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| 220 |
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| 221 | MHMatrix matrix(mat, &rules, "MatrixTrain");
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| 222 |
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| 223 | MParList plist;
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| 224 | MTaskList tlist;
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| 225 | plist.AddToList(&tlist);
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| 226 | plist.AddToList(&matrix);
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| 227 |
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| 228 | MRanForest rf;
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| 229 | rf.SetNumTrees(fNumTrees);
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| 230 | rf.SetNumTry(fNumTry);
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| 231 | rf.SetNdSize(fNdSize);
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| 232 | rf.SetClassify(ver<3 ? kTRUE : kFALSE);
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| 233 | if (ver==1)
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| 234 | rf.SetGrid(grid);
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| 235 | if (fLastDataColumnHasWeights)
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| 236 | rf.SetWeights(weights);
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| 237 |
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| 238 | plist.AddToList(&rf);
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| 239 |
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| 240 | MRanForestGrow rfgrow;
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| 241 | tlist.AddToList(&rfgrow);
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| 242 |
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| 243 | MFillH fillh("MHRanForestGini");
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| 244 | tlist.AddToList(&fillh);
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| 245 |
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| 246 | MEvtLoop evtloop(fTitle);
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| 247 | evtloop.SetParList(&plist);
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| 248 | evtloop.SetDisplay(fDisplay);
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| 249 | evtloop.SetLogStream(fLog);
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| 250 |
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| 251 | if (!evtloop.Eventloop())
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| 252 | return kFALSE;
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| 253 |
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| 254 | if (fDebug)
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| 255 | gLog.SetNullOutput(kFALSE);
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| 256 |
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| 257 | if (ver==0)
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| 258 | {
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| 259 | // Calculate bin center
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| 260 | const Double_t E = (TMath::Log10(grid[ie])+TMath::Log10(grid[ie+1]))/2;
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| 261 |
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| 262 | // save whole forest
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| 263 | rf.SetUserVal(E);
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| 264 | rf.SetName(Form("%.10f", E));
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| 265 | }
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| 266 |
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| 267 | rf.Write();
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| 268 | }
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| 269 |
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| 270 | // save rules
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| 271 | usedrules.Write("rules");
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| 272 |
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| 273 | fFunc.Write(gsNameEvalFunc);
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| 274 |
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| 275 | return kTRUE;
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| 276 | }
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| 277 |
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| 278 | Int_t MRanForestCalc::ReadForests(MParList &plist)
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| 279 | {
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| 280 | TFile fileRF(fFileName, "read");
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| 281 | if (!fileRF.IsOpen())
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| 282 | {
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| 283 | *fLog << err << dbginf << "File containing RFs could not be opened... aborting." << endl;
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| 284 | return kFALSE;
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| 285 | }
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| 286 |
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| 287 | fEForests.Delete();
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| 288 |
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| 289 | TIter Next(fileRF.GetListOfKeys());
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| 290 | TObject *o=0;
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| 291 | while ((o=Next()))
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| 292 | {
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| 293 | MRanForest *forest=0;
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| 294 | fileRF.GetObject(o->GetName(), forest);
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| 295 | if (!forest)
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| 296 | continue;
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| 297 |
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| 298 | forest->SetUserVal(atof(o->GetName()));
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| 299 |
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| 300 | fEForests.Add(forest);
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| 301 | }
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| 302 |
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| 303 | // Maybe fEForests[0].fRules could be used instead?
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| 304 | if (fData->Read("rules")<=0)
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| 305 | {
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| 306 | *fLog << err << "ERROR - Reading 'rules' from file " << fFileName << endl;
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| 307 | return kFALSE;
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| 308 | }
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| 309 |
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| 310 | if (fileRF.GetListOfKeys()->FindObject(gsNameEvalFunc))
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| 311 | {
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| 312 | if (fFunc.Read(gsNameEvalFunc)<=0)
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| 313 | {
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| 314 | *fLog << err << "ERROR - Reading '" << gsNameEvalFunc << "' from file " << fFileName << endl;
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| 315 | return kFALSE;
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| 316 | }
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| 317 |
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| 318 | *fLog << inf << "Evaluation function found in file: " << fFunc.GetRule() << endl;
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| 319 | }
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| 320 |
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| 321 | return kTRUE;
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| 322 | }
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| 323 |
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| 324 | Int_t MRanForestCalc::PreProcess(MParList *plist)
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| 325 | {
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| 326 | fRFOut = (MParameterD*)plist->FindCreateObj("MParameterD", fNameOutput);
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| 327 | if (!fRFOut)
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| 328 | return kFALSE;
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| 329 |
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| 330 | fData = (MDataArray*)plist->FindCreateObj("MDataArray");
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| 331 | if (!fData)
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| 332 | return kFALSE;
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| 333 |
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| 334 | if (!ReadForests(*plist))
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| 335 | {
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| 336 | *fLog << err << "Reading RFs failed... aborting." << endl;
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| 337 | return kFALSE;
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| 338 | }
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| 339 |
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| 340 | *fLog << inf << "RF read from " << fFileName << endl;
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| 341 |
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| 342 | if (!fFunc.PreProcess(plist))
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| 343 | {
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| 344 | *fLog << err << "PreProcessing of evaluation function failed... aborting." << endl;
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| 345 | return kFALSE;
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| 346 | }
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| 347 |
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| 348 | if (fTestMatrix)
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| 349 | return kTRUE;
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| 350 |
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| 351 | fData->Print();
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| 352 |
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| 353 | if (!fData->PreProcess(plist))
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| 354 | {
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| 355 | *fLog << err << "PreProcessing of the MDataArray failed... aborting." << endl;
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| 356 | return kFALSE;
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| 357 | }
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| 358 |
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| 359 | return kTRUE;
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| 360 | }
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| 361 |
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| 362 | Double_t MRanForestCalc::Eval() const
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| 363 | {
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| 364 | TVector event;
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| 365 | if (fTestMatrix)
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| 366 | *fTestMatrix >> event;
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| 367 | else
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| 368 | *fData >> event;
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| 369 |
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| 370 | // --------------- Single Tree RF -------------------
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| 371 | if (fEForests.GetEntriesFast()==1)
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| 372 | {
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| 373 | MRanForest *rf = static_cast<MRanForest*>(fEForests.UncheckedAt(0));
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| 374 | return rf->CalcHadroness(event);
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| 375 | }
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| 376 |
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| 377 | // --------------- Multi Tree RF -------------------
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| 378 | static TF1 f1("f1", "gaus");
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| 379 |
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| 380 | Double_t sume = 0;
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| 381 | Double_t sumh = 0;
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| 382 | Double_t maxh = 0;
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| 383 | Double_t maxe = 0;
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| 384 |
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| 385 | Double_t max = -1e10;
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| 386 | Double_t min = 1e10;
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| 387 |
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| 388 | TIter Next(&fEForests);
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| 389 | MRanForest *rf = 0;
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| 390 |
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| 391 | TGraph g;
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| 392 | while ((rf=(MRanForest*)Next()))
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| 393 | {
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| 394 | const Double_t h = rf->CalcHadroness(event);
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| 395 | const Double_t e = rf->GetUserVal();
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| 396 |
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| 397 | g.SetPoint(g.GetN(), e, h);
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| 398 |
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| 399 | sume += e*h;
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| 400 | sumh += h;
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| 401 |
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| 402 | if (h>maxh)
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| 403 | {
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| 404 | maxh = h;
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| 405 | maxe = e;
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| 406 | }
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| 407 | if (e>max)
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| 408 | max = e;
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| 409 | if (e<min)
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| 410 | min = e;
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| 411 | }
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| 412 |
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| 413 | switch (fEstimationMode)
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| 414 | {
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| 415 | case kMean:
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| 416 | return sume/sumh;
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| 417 | case kMaximum:
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| 418 | return maxe;
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| 419 | case kFit:
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| 420 | f1.SetParameter(0, maxh);
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| 421 | f1.SetParameter(1, maxe);
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| 422 | f1.SetParameter(2, 0.125);
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| 423 | g.Fit(&f1, "Q0N");
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| 424 | return f1.GetParameter(1);
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| 425 | }
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| 426 |
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| 427 | return 0;
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| 428 | }
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| 429 |
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| 430 | Int_t MRanForestCalc::Process()
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| 431 | {
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| 432 | const Double_t val = Eval();
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| 433 |
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| 434 | fRFOut->SetVal(fFunc.Eval(val));
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| 435 | fRFOut->SetReadyToSave();
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| 436 |
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| 437 | return kTRUE;
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| 438 | }
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| 439 |
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| 440 | void MRanForestCalc::Print(Option_t *o) const
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| 441 | {
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| 442 | *fLog << all;
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| 443 | *fLog << GetDescriptor() << ":" << endl;
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| 444 | *fLog << " - Forest ";
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| 445 | switch (fEForests.GetEntries())
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| 446 | {
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| 447 | case 0: *fLog << "not yet initialized." << endl; break;
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| 448 | case 1: *fLog << "is a single tree forest." << endl; break;
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| 449 | default: *fLog << "is a multi tree forest." << endl; break;
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|---|
| 450 | }
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|---|
| 451 | /*
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| 452 | *fLog << " - Trees: " << fNumTrees << endl;
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| 453 | *fLog << " - Trys: " << fNumTry << endl;
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|---|
| 454 | *fLog << " - Node Size: " << fNdSize << endl;
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|---|
| 455 | *fLog << " - Node Size: " << fNdSize << endl;
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|---|
| 456 | */
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|---|
| 457 | *fLog << " - FileName: " << fFileName << endl;
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|---|
| 458 | *fLog << " - NameOutput: " << fNameOutput << endl;
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|---|
| 459 | }
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|---|
| 460 |
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|---|
| 461 | // --------------------------------------------------------------------------
|
|---|
| 462 | //
|
|---|
| 463 | //
|
|---|
| 464 | Int_t MRanForestCalc::ReadEnv(const TEnv &env, TString prefix, Bool_t print)
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|---|
| 465 | {
|
|---|
| 466 | Bool_t rc = kFALSE;
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|---|
| 467 | if (IsEnvDefined(env, prefix, "FileName", print))
|
|---|
| 468 | {
|
|---|
| 469 | rc = kTRUE;
|
|---|
| 470 | SetFileName(GetEnvValue(env, prefix, "FileName", fFileName));
|
|---|
| 471 | }
|
|---|
| 472 | if (IsEnvDefined(env, prefix, "Debug", print))
|
|---|
| 473 | {
|
|---|
| 474 | rc = kTRUE;
|
|---|
| 475 | SetDebug(GetEnvValue(env, prefix, "Debug", fDebug));
|
|---|
| 476 | }
|
|---|
| 477 | if (IsEnvDefined(env, prefix, "NameOutput", print))
|
|---|
| 478 | {
|
|---|
| 479 | rc = kTRUE;
|
|---|
| 480 | SetNameOutput(GetEnvValue(env, prefix, "NameOutput", fNameOutput));
|
|---|
| 481 | }
|
|---|
| 482 | if (IsEnvDefined(env, prefix, "EstimationMode", print))
|
|---|
| 483 | {
|
|---|
| 484 | TString txt = GetEnvValue(env, prefix, "EstimationMode", "");
|
|---|
| 485 | txt = txt.Strip(TString::kBoth);
|
|---|
| 486 | txt.ToLower();
|
|---|
| 487 | if (txt==(TString)"mean")
|
|---|
| 488 | fEstimationMode = kMean;
|
|---|
| 489 | if (txt==(TString)"maximum")
|
|---|
| 490 | fEstimationMode = kMaximum;
|
|---|
| 491 | if (txt==(TString)"fit")
|
|---|
| 492 | fEstimationMode = kFit;
|
|---|
| 493 | rc = kTRUE;
|
|---|
| 494 | }
|
|---|
| 495 | return rc;
|
|---|
| 496 | }
|
|---|
