correlationscattersMultiClass.cxx

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#include "TMVA/correlationscattersMultiClass.h"
#include "TMVA/Config.h"
 
 
 
// this macro plots the correlations (as scatter plots) of
// the various input variable combinations used in TMVA (e.g. running
// TMVAnalysis.C).  Signal and Background are plotted separately
 
// input: - Input file (result from TMVA),
//        - normal/decorrelated/PCA
//        - use of TMVA plotting TStyle
void TMVA::correlationscattersMultiClass(TString dataset, TString fin , TString var,
                                         TString dirName_ , TString /*title*/,
                                         Bool_t /*isRegression */,
                                         Bool_t useTMVAStyle )
{
   // set style and remove existing canvas'
   TMVAGlob::Initialize( useTMVAStyle );
 
   TString extension = dirName_;
   extension.ReplaceAll( "InputVariables", "" );
   extension.ReplaceAll( " ", "" );
   if (extension == "") extension = "_Id"; // use 'Id' for 'idendtity transform'
 
   var.ReplaceAll( extension, "" );
   cout << "Called macro \"correlationscattersMultiClass\" for variable: \"" << var
        << "\", transformation type \"" << dirName_
        << "\" (extension: \"" << extension << "\")" << endl;
 
   // checks if file with name "fin" is already open, and if not opens one
   TFile* file = TMVAGlob::OpenFile( fin );
 
   TString dirName = dirName_ + "/CorrelationPlots";
 
   // find out number of input variables
   TDirectory* vardir = (TDirectory*)file->GetDirectory(dataset.Data())->Get( "InputVariables_Id" );
   if (!vardir) {
      cout << "ERROR: no such directory: \"InputVariables\"" << endl;
      return;
   }
   Int_t noVars = TMVAGlob::GetNumberOfInputVariablesMultiClass( vardir );
 
   TDirectory* dir = (TDirectory*)file->GetDirectory(dataset.Data())->Get( dirName );
   if (dir==0) {
      cout << "No information about " << extension << " available in " << fin << endl;
      return;
   }
   dir->cd();
 
   TListIter keyIt(dir->GetListOfKeys());
   Int_t noPlots = noVars - 1;
 
   cout << "noPlots: " << noPlots << " --> noVars: " << noVars << endl;
   if (noVars != Int_t(noVars)) {
      cout << "*** Warning: problem in inferred number of variables ... not an integer *** " << endl;
   }
 
   // define Canvas layout here!
   // default setting
   Int_t xPad;  // no of plots in x
   Int_t yPad;  // no of plots in y
   Int_t width; // size of canvas
   Int_t height;
   switch (noPlots) {
   case 1:
      xPad = 1; yPad = 1; width = 400; height = width; break;
   case 2:
      xPad = 2; yPad = 1; width = 700; height = 0.55*width; break;
   case 3:
      xPad = 3; yPad = 1; width = 800; height = 0.4*width; break;
   case 4:
      xPad = 2; yPad = 2; width = 600; height = width; break;
   default:
      xPad = 3; yPad = 2; width = 800; height = 0.55*width; break;
   }
   Int_t noPadPerCanv = xPad * yPad ;
 
   // counter variables
   Int_t countCanvas = 0;
 
   // loop over all objects in "input_variables" directory
 
   std::vector<TString> classnames = TMVAGlob::GetClassNames(dir);
   UInt_t ncls = classnames.end()-classnames.begin();
 
   for (UInt_t itype = 0; itype < ncls; itype++) {
 
      TIter next(gDirectory->GetListOfKeys());
      TKey   * key  = 0;
      TCanvas* canv = 0;
 
      Int_t countPad    = 0;
 
      while ( (key = (TKey*)next()) ) {
 
         if (key->GetCycle() != 1) continue;
 
         // make sure, that we only look at histograms
         TClass *cl = gROOT->GetClass(key->GetClassName());
         if (!cl->InheritsFrom("TH1")) continue;
         TH1 *scat = (TH1*)key->ReadObj();
         TString hname = scat->GetName();
 
         //std::cout << classnames[itype] << " " << extension << "" << hname << " " << var << std::endl;
         // check for scatter plots
         if (! (hname.EndsWith( classnames[itype] + extension ) &&
                hname.Contains( TString("_") + var + "_" ) && hname.BeginsWith("scat_")) ) {
            scat->Delete();
            continue;
         }
 
         // found a new signal plot
 
         // create new canvas
         if (countPad%noPadPerCanv==0) {
            ++countCanvas;
            TString ext = extension; ext.Remove( 0, 1 );
            canv = new TCanvas( TString::Format("canvas%d", countCanvas),
                                TString::Format("Correlation profiles for '%s'-transformed variables (%s)",
                                     ext.Data(), classnames[itype].Data()),
                                countCanvas*50+200, countCanvas*20, width, height );
            canv->Divide(xPad,yPad);
         }
 
         if (!canv) continue;
 
         canv->cd(countPad++%noPadPerCanv+1);
 
         // find the corredponding profile plot
         TString bgname = hname;
         bgname.ReplaceAll("scat_","prof_");
         TH1 *prof = (TH1*)gDirectory->Get(bgname);
         if (prof == NULL) {
            cout << "ERROR!!! couldn't find profile plot for" << hname << endl;
            //exit(1);
            return;
         }
         // this is set but not stored during plot creation in MVA_Factory
         TMVAGlob::SetSignalAndBackgroundStyle( scat, prof );
 
         // chop off "signal"
         TMVAGlob::SetFrameStyle( scat, 1.2 );
 
         // normalise both signal and background
         scat->Scale( 1.0/scat->GetSumOfWeights() );
 
         // finally plot and overlay
         // Float_t sc = 1.1;
         // if (countPad==2) sc = 1.3;
         scat->SetMarkerColor(  4);
         scat->Draw("col");
         prof->SetMarkerColor( gConfig().fVariablePlotting.fUsePaperStyle ? 1 : 2  );
         prof->SetMarkerSize( 0.2 );
         prof->SetLineColor( gConfig().fVariablePlotting.fUsePaperStyle ? 1 : 2 );
         prof->SetLineWidth( gConfig().fVariablePlotting.fUsePaperStyle ? 2 : 1 );
         prof->SetFillStyle( 3002 );
         prof->SetFillColor( 46 );
         prof->Draw("samee1");
         // redraw axes
         scat->Draw("sameaxis");
 
         // save canvas to file
         if (countPad%noPadPerCanv==0) {
            canv->Update();
 
            TString fname = dataset+TString::Format( "/plots/correlationscatter_%s_%s_c%i",var.Data(), extension.Data(), countCanvas );
            TMVAGlob::plot_logo();
            TMVAGlob::imgconv( canv, fname );
         }
      }
      if (countPad%noPadPerCanv!=0) {
         canv->Update();
 
         TString fname = dataset+TString::Format( "/plots/correlationscatter_%s_%s_c%i",var.Data(), extension.Data(), countCanvas );
         TMVAGlob::plot_logo();
         TMVAGlob::imgconv( canv, fname );
      }
   }
}