| 1 | /* ======================================================================== *\
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| 2 | ! $Name: not supported by cvs2svn $:$Id: MRanForestCalc.cc,v 1.25 2006-11-02 08:57:00 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 |
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| 67 | MRanForestCalc::MRanForestCalc(const char *name, const char *title)
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| 68 | : fData(0), fRFOut(0), fTestMatrix(0),
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| 69 | fNumTrees(-1), fNumTry(-1), fNdSize(-1), fNumObsoleteVariables(1),
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| 70 | fLastDataColumnHasWeights(kFALSE),
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| 71 | fNameOutput(gsNameOutput), fDebug(kFALSE), fEstimationMode(kMean)
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| 72 | {
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| 73 | fName = name ? name : gsDefName.Data();
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| 74 | fTitle = title ? title : gsDefTitle.Data();
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| 75 |
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| 76 | // FIXME:
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| 77 | fNumTrees = 100; //100
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| 78 | fNumTry = 0; //3 0 means: in MRanForest estimated best value will be calculated
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| 79 | fNdSize = 1; //1
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| 80 | }
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| 81 |
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| 82 | MRanForestCalc::~MRanForestCalc()
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| 83 | {
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| 84 | fEForests.Delete();
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| 85 | }
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| 86 |
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| 87 | // --------------------------------------------------------------------------
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| 88 | //
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| 89 | // ver=0: One yes/no-classification forest is trained for each bin.
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| 90 | // the yes/no classification is done using the grid
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| 91 | // ver=1: One classification forest is trained. The last column contains a
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| 92 | // value which is turned into a classifier by rf itself using the grid
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| 93 | // ver=2: One classification forest is trained. The last column already contains
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| 94 | // the classifier
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| 95 | // ver=3: A regression forest is trained. The last column contains the
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| 96 | // classifier
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| 97 | //
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| 98 | Int_t MRanForestCalc::Train(const MHMatrix &matrixtrain, const TArrayD &grid, Int_t ver)
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| 99 | {
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| 100 | gLog.Separator("MRanForestCalc - Train");
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| 101 |
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| 102 | if (!matrixtrain.GetColumns())
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| 103 | {
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| 104 | *fLog << err << "ERROR - MHMatrix does not contain rules... abort." << endl;
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| 105 | return kFALSE;
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| 106 | }
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| 107 |
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| 108 | const Int_t ncols = matrixtrain.GetM().GetNcols();
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| 109 | const Int_t nrows = matrixtrain.GetM().GetNrows();
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| 110 | if (ncols<=0 || nrows <=0)
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| 111 | {
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| 112 | *fLog << err << "ERROR - No. of columns or no. of rows of matrixtrain equal 0 ... abort." << endl;
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| 113 | return kFALSE;
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| 114 | }
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| 115 |
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| 116 | // rules (= combination of image par) to be used for energy estimation
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| 117 | TFile fileRF(fFileName, "recreate");
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| 118 | if (!fileRF.IsOpen())
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| 119 | {
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| 120 | *fLog << err << "ERROR - File to store RFs could not be opened... abort." << endl;
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| 121 | return kFALSE;
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| 122 | }
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| 123 |
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| 124 | // The number of columns which have to be removed for the training
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| 125 | // The last data column may contain weight which also have to be removed
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| 126 | const Int_t nobs = fNumObsoleteVariables + (fLastDataColumnHasWeights?1:0); // Number of obsolete columns
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| 127 |
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| 128 | const MDataArray &dcol = *matrixtrain.GetColumns();
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| 129 |
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| 130 | // Make a copy of the rules for accessing the train-data
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| 131 | MDataArray usedrules;
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| 132 | for (Int_t i=0; i<ncols; i++)
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| 133 | if (i<ncols-nobs) // -3 is important!!!
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| 134 | usedrules.AddEntry(dcol[i].GetRule());
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| 135 | else
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| 136 | *fLog << inf << "Skipping " << dcol[i].GetRule() << " for training" << endl;
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| 137 |
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| 138 | // In the case of regression store the rule to be regessed in the
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| 139 | // last entry of your rules
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| 140 | MDataArray rules(usedrules);
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| 141 | rules.AddEntry(ver<3?"Classification.fVal":dcol[ncols-1].GetRule().Data());
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| 142 |
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| 143 | // prepare train-matrix finally used
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| 144 | TMatrix mat(matrixtrain.GetM());
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| 145 |
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| 146 | // Resize it such that the obsolete columns are removed
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| 147 | mat.ResizeTo(nrows, ncols-nobs+1);
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| 148 |
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| 149 | if (fDebug)
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| 150 | gLog.SetNullOutput(kTRUE);
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| 151 |
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| 152 | // In the case one independant RF is trained for each bin (e.g.
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| 153 | // energy-bin) train all of them
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| 154 | const Int_t nbins = ver>0 ? 1 : grid.GetSize()-1;
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| 155 | for (Int_t ie=0; ie<nbins; ie++)
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| 156 | {
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| 157 | // In the case weights should be used initialize the
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| 158 | // corresponding array
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| 159 | TArrayF weights(nrows);
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| 160 | if (fLastDataColumnHasWeights)
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| 161 | for (Int_t j=0; j<nrows; j++)
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| 162 | {
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| 163 | weights[j] = matrixtrain.GetM()(j, ncols-nobs);
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| 164 | if (j%250==0)
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| 165 | cout << weights[j] << " ";
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| 166 | }
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| 167 |
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| 168 | // Setup the matrix such that the last comlumn contains
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| 169 | // the classifier or the regeression target value
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| 170 | switch (ver)
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| 171 | {
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| 172 | case 0: // Replace last column by a classification which is 1 in
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| 173 | // the case the event belongs to this bin, 0 otherwise
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| 174 | {
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| 175 | Int_t irows=0;
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| 176 | for (Int_t j=0; j<nrows; j++)
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| 177 | {
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| 178 | const Double_t value = matrixtrain.GetM()(j,ncols-1);
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| 179 | const Bool_t inside = value>grid[ie] && value<=grid[ie+1];
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| 180 |
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| 181 | mat(j, ncols-nobs) = inside ? 1 : 0;
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| 182 |
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| 183 | if (inside)
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| 184 | irows++;
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| 185 | }
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| 186 | if (irows==0)
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| 187 | *fLog << warn << "WARNING - Skipping";
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| 188 | else
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| 189 | *fLog << inf << "Training RF for";
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| 190 |
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| 191 | *fLog << " bin " << ie << " (" << grid[ie] << ", " << grid[ie+1] << ") " << irows << "/" << nrows << endl;
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| 192 |
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| 193 | if (irows==0)
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| 194 | continue;
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| 195 | }
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| 196 | break;
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| 197 |
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| 198 | case 1: // Use last column as classifier or for regression
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| 199 | case 2:
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| 200 | case 3:
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| 201 | for (Int_t j=0; j<nrows; j++)
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| 202 | mat(j, ncols-nobs) = matrixtrain.GetM()(j,ncols-1);
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| 203 | break;
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| 204 | }
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| 205 |
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| 206 | MHMatrix matrix(mat, &rules, "MatrixTrain");
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| 207 |
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| 208 | MParList plist;
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| 209 | MTaskList tlist;
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| 210 | plist.AddToList(&tlist);
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| 211 | plist.AddToList(&matrix);
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| 212 |
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| 213 | MRanForest rf;
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| 214 | rf.SetNumTrees(fNumTrees);
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| 215 | rf.SetNumTry(fNumTry);
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| 216 | rf.SetNdSize(fNdSize);
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| 217 | rf.SetClassify(ver<3 ? kTRUE : kFALSE);
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| 218 | if (ver==1)
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| 219 | rf.SetGrid(grid);
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| 220 | if (fLastDataColumnHasWeights)
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| 221 | rf.SetWeights(weights);
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| 222 |
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| 223 | plist.AddToList(&rf);
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| 224 |
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| 225 | MRanForestGrow rfgrow;
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| 226 | tlist.AddToList(&rfgrow);
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| 227 |
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| 228 | MFillH fillh("MHRanForestGini");
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| 229 | tlist.AddToList(&fillh);
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| 230 |
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| 231 | MEvtLoop evtloop;
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| 232 | evtloop.SetParList(&plist);
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| 233 | evtloop.SetDisplay(fDisplay);
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| 234 | evtloop.SetLogStream(fLog);
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| 235 |
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| 236 | if (!evtloop.Eventloop())
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| 237 | return kFALSE;
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| 238 |
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| 239 | if (fDebug)
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| 240 | gLog.SetNullOutput(kFALSE);
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| 241 |
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| 242 | if (ver==0)
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| 243 | {
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| 244 | // Calculate bin center
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| 245 | const Double_t E = (TMath::Log10(grid[ie])+TMath::Log10(grid[ie+1]))/2;
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| 246 |
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| 247 | // save whole forest
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| 248 | rf.SetUserVal(E);
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| 249 | rf.SetName(Form("%.10f", E));
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| 250 | }
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| 251 |
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| 252 | rf.Write();
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| 253 | }
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| 254 |
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| 255 | // save rules
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| 256 | usedrules.Write("rules");
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| 257 |
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| 258 | return kTRUE;
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| 259 | }
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| 260 |
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| 261 | Int_t MRanForestCalc::ReadForests(MParList &plist)
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| 262 | {
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| 263 | TFile fileRF(fFileName, "read");
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| 264 | if (!fileRF.IsOpen())
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| 265 | {
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| 266 | *fLog << err << dbginf << "File containing RFs could not be opened... aborting." << endl;
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| 267 | return kFALSE;
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| 268 | }
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| 269 |
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| 270 | fEForests.Delete();
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| 271 |
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| 272 | TIter Next(fileRF.GetListOfKeys());
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| 273 | TObject *o=0;
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| 274 | while ((o=Next()))
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| 275 | {
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| 276 | MRanForest *forest=0;
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| 277 | fileRF.GetObject(o->GetName(), forest);
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| 278 | if (!forest)
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| 279 | continue;
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| 280 |
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| 281 | forest->SetUserVal(atof(o->GetName()));
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| 282 |
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| 283 | fEForests.Add(forest);
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| 284 | }
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| 285 |
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| 286 | // Maybe fEForests[0].fRules could be used instead?
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| 287 | if (fData->Read("rules")<=0)
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| 288 | {
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| 289 | *fLog << err << "ERROR - Reading 'rules' from file " << fFileName << endl;
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| 290 | return kFALSE;
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| 291 | }
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| 292 |
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| 293 | return kTRUE;
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| 294 | }
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| 295 |
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| 296 | Int_t MRanForestCalc::PreProcess(MParList *plist)
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| 297 | {
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| 298 | fRFOut = (MParameterD*)plist->FindCreateObj("MParameterD", fNameOutput);
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| 299 | if (!fRFOut)
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| 300 | return kFALSE;
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| 301 |
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| 302 | fData = (MDataArray*)plist->FindCreateObj("MDataArray");
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| 303 | if (!fData)
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| 304 | return kFALSE;
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| 305 |
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| 306 | if (!ReadForests(*plist))
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| 307 | {
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| 308 | *fLog << err << "Reading RFs failed... aborting." << endl;
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| 309 | return kFALSE;
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| 310 | }
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| 311 |
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| 312 | *fLog << inf << "RF read from " << fFileName << endl;
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| 313 |
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| 314 | if (fTestMatrix)
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| 315 | return kTRUE;
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| 316 |
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| 317 | fData->Print();
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| 318 |
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| 319 | if (!fData->PreProcess(plist))
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| 320 | {
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| 321 | *fLog << err << "PreProcessing of the MDataArray failed... aborting." << endl;
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| 322 | return kFALSE;
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| 323 | }
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| 324 |
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| 325 | return kTRUE;
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| 326 | }
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| 327 |
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| 328 | Int_t MRanForestCalc::Process()
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| 329 | {
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| 330 | TVector event;
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| 331 | if (fTestMatrix)
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| 332 | *fTestMatrix >> event;
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| 333 | else
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| 334 | *fData >> event;
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| 335 |
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| 336 | // --------------- Single Tree RF -------------------
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| 337 | if (fEForests.GetEntriesFast()==1)
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| 338 | {
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| 339 | MRanForest *rf = static_cast<MRanForest*>(fEForests.UncheckedAt(0));
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| 340 | fRFOut->SetVal(rf->CalcHadroness(event));
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| 341 | fRFOut->SetReadyToSave();
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| 342 |
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| 343 | return kTRUE;
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| 344 | }
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| 345 |
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| 346 | // --------------- Multi Tree RF -------------------
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| 347 | static TF1 f1("f1", "gaus");
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| 348 |
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| 349 | Double_t sume = 0;
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| 350 | Double_t sumh = 0;
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| 351 | Double_t maxh = 0;
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| 352 | Double_t maxe = 0;
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| 353 |
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| 354 | Double_t max = -1e10;
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| 355 | Double_t min = 1e10;
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| 356 |
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| 357 | TIter Next(&fEForests);
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| 358 | MRanForest *rf = 0;
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| 359 |
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| 360 | TGraph g;
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| 361 | while ((rf=(MRanForest*)Next()))
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| 362 | {
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| 363 | const Double_t h = rf->CalcHadroness(event);
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| 364 | const Double_t e = rf->GetUserVal();
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| 365 |
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| 366 | g.SetPoint(g.GetN(), e, h);
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| 367 |
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| 368 | sume += e*h;
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| 369 | sumh += h;
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| 370 |
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| 371 | if (h>maxh)
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| 372 | {
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| 373 | maxh = h;
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| 374 | maxe = e;
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| 375 | }
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| 376 | if (e>max)
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| 377 | max = e;
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| 378 | if (e<min)
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| 379 | min = e;
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| 380 | }
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| 381 |
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| 382 | switch (fEstimationMode)
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| 383 | {
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| 384 | case kMean:
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| 385 | fRFOut->SetVal(pow(10, sume/sumh));
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| 386 | break;
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| 387 | case kMaximum:
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| 388 | fRFOut->SetVal(pow(10, maxe));
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| 389 | break;
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| 390 | case kFit:
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| 391 | f1.SetParameter(0, maxh);
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| 392 | f1.SetParameter(1, maxe);
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| 393 | f1.SetParameter(2, 0.125);
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| 394 | g.Fit(&f1, "Q0N");
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| 395 | fRFOut->SetVal(pow(10, f1.GetParameter(1)));
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| 396 | break;
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| 397 | }
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| 398 |
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| 399 | fRFOut->SetReadyToSave();
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| 400 |
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| 401 | return kTRUE;
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| 402 | }
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| 403 |
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| 404 | void MRanForestCalc::Print(Option_t *o) const
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| 405 | {
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| 406 | *fLog << all;
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| 407 | *fLog << GetDescriptor() << ":" << endl;
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| 408 | *fLog << " - Forest ";
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| 409 | switch (fEForests.GetEntries())
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| 410 | {
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| 411 | case 0: *fLog << "not yet initialized." << endl; break;
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| 412 | case 1: *fLog << "is a single tree forest." << endl; break;
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| 413 | default: *fLog << "is a multi tree forest." << endl; break;
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| 414 | }
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| 415 | /*
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| 416 | *fLog << " - Trees: " << fNumTrees << endl;
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| 417 | *fLog << " - Trys: " << fNumTry << endl;
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| 418 | *fLog << " - Node Size: " << fNdSize << endl;
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| 419 | *fLog << " - Node Size: " << fNdSize << endl;
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| 420 | */
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| 421 | *fLog << " - FileName: " << fFileName << endl;
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| 422 | *fLog << " - NameOutput: " << fNameOutput << endl;
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| 423 | }
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| 424 |
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| 425 | // --------------------------------------------------------------------------
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| 426 | //
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| 427 | //
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| 428 | Int_t MRanForestCalc::ReadEnv(const TEnv &env, TString prefix, Bool_t print)
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| 429 | {
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| 430 | Bool_t rc = kFALSE;
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| 431 | if (IsEnvDefined(env, prefix, "FileName", print))
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| 432 | {
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| 433 | rc = kTRUE;
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| 434 | SetFileName(GetEnvValue(env, prefix, "FileName", fFileName));
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| 435 | }
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| 436 | if (IsEnvDefined(env, prefix, "Debug", print))
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| 437 | {
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| 438 | rc = kTRUE;
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| 439 | SetDebug(GetEnvValue(env, prefix, "Debug", fDebug));
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| 440 | }
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| 441 | if (IsEnvDefined(env, prefix, "NameOutput", print))
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| 442 | {
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| 443 | rc = kTRUE;
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| 444 | SetNameOutput(GetEnvValue(env, prefix, "NameOutput", fNameOutput));
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| 445 | }
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| 446 | if (IsEnvDefined(env, prefix, "EstimationMode", print))
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| 447 | {
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| 448 | TString txt = GetEnvValue(env, prefix, "EstimationMode", "");
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| 449 | txt = txt.Strip(TString::kBoth);
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| 450 | txt.ToLower();
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| 451 | if (txt==(TString)"mean")
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| 452 | fEstimationMode = kMean;
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| 453 | if (txt==(TString)"maximum")
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| 454 | fEstimationMode = kMaximum;
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| 455 | if (txt==(TString)"fit")
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| 456 | fEstimationMode = kFit;
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| 457 | rc = kTRUE;
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|---|
| 458 | }
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| 459 | return rc;
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|---|
| 460 | }
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|---|
