variables.cxx

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#include "TMVA/variables.h"
 
 
// this macro plots the distributions of the different input variables
// used in TMVA (e.g. running TMVAnalysis.C).  Signal and Background are overlayed.
 
// input: - Input file (result from TMVA),
//        - normal/decorrelated/PCA
//        - use of TMVA plotting TStyle
void TMVA::variables(TString dataset, TString fin, TString dirName , TString title ,
                     Bool_t isRegression, Bool_t useTMVAStyle )
{
   TString outfname = dirName;
   outfname.ToLower(); outfname.ReplaceAll( "input", ""  );
 
   // set style and remove existing canvas'
   TMVAGlob::Initialize( useTMVAStyle );
 
   // obtain shorter histogram title 
   TString htitle = title; 
   htitle.ReplaceAll("variables ","variable");
   htitle.ReplaceAll("and target(s)","");
   htitle.ReplaceAll("(training sample)","");
 
   // checks if file with name "fin" is already open, and if not opens one
   TFile* file = TMVAGlob::OpenFile( fin );
 
   TDirectory* dir = (TDirectory*)file->GetDirectory(dataset.Data())->Get(dirName );
   if (dir==0) {
      cout << "No information about " << title << " available in directory " << dirName << " of file " << fin << endl;
      return;
   }
   dir->cd();
 
   // how many plots are in the directory?
   Int_t noPlots = TMVAGlob::GetNumberOfInputVariables( dir ) +
      TMVAGlob::GetNumberOfTargets( dir );
 
   // 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 = 550; height = 0.90*width; break;
   case 2:
      xPad = 2; yPad = 1; width = 600; height = 0.50*width; break;
   case 3:
      xPad = 3; yPad = 1; width = 900; 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;
   Int_t countPad    = 0;
 
   // loop over all objects in directory
   TCanvas* canv = 0;
   TKey*    key  = 0;
   Bool_t   createNewFig = kFALSE;
   TIter next(dir->GetListOfKeys());
   while ((key = (TKey*)next())) {
      if (key->GetCycle() != 1) continue;
 
      if (!TString(key->GetName()).Contains("__Signal") && 
          !(isRegression && TString(key->GetName()).Contains("__Regression"))) continue;
 
      // make sure, that we only look at histograms
      TClass *cl = gROOT->GetClass(key->GetClassName());
      if (!cl->InheritsFrom("TH1")) continue;
      TH1 *sig = (TH1*)key->ReadObj();
      TString hname(sig->GetName());
 
      // create new canvas
      if (countPad%noPadPerCanv==0) {
         ++countCanvas;
         canv = new TCanvas( Form("canvas%d", countCanvas), title,
                             countCanvas*50+50, countCanvas*20, width, height );
         canv->Divide(xPad,yPad);
         canv->Draw();
      }
 
      TPad* cPad = (TPad*)canv->cd(countPad++%noPadPerCanv+1);
      
      // find the corredponding backgrouns histo
      TString bgname = hname;
      bgname.ReplaceAll("__Signal","__Background");
      TH1 *bgd = (TH1*)dir->Get(bgname);
      if (bgd == NULL) {
         cout << "ERROR!!! couldn't find background histo for" << hname << endl;
         return;
      }
 
      // this is set but not stored during plot creation in MVA_Factory
      TMVAGlob::SetSignalAndBackgroundStyle( sig, (isRegression ? 0 : bgd) );            
 
      sig->SetTitle( TString( htitle ) + ": " + sig->GetTitle() );
      TMVAGlob::SetFrameStyle( sig, 1.2 );
 
      // normalise both signal and background
      if (!isRegression) TMVAGlob::NormalizeHists( sig, bgd );
      else {
         // change histogram title for target
         TString nme = sig->GetName();
         if (nme.Contains( "_target" )) {
            TString tit = sig->GetTitle();
            sig->SetTitle( tit.ReplaceAll("Input variable", "Regression target" ) );
         }
      }
 
      // finally plot and overlay
      Float_t sc = 1.1;
      if (countPad == 1) sc = 1.3;
      sig->SetMaximum( TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*sc );
      sig->Draw( "hist" );
      cPad->SetLeftMargin( 0.17 );
 
      sig->GetYaxis()->SetTitleOffset( 1.70 );
      if (!isRegression) {
         bgd->Draw("histsame");
         TString ytit = TString("(1/N) ") + sig->GetYaxis()->GetTitle();
         sig->GetYaxis()->SetTitle( ytit ); // histograms are normalised
      }
 
      // Draw legend
      if (countPad == 1 && !isRegression) {
         TLegend *legend= new TLegend( cPad->GetLeftMargin(), 
                                       1-cPad->GetTopMargin()-.15, 
                                       cPad->GetLeftMargin()+.4, 
                                       1-cPad->GetTopMargin() );
         legend->SetFillStyle(1);
         legend->AddEntry(sig,"Signal","F");
         legend->AddEntry(bgd,"Background","F");
         legend->SetBorderSize(1);
         legend->SetMargin( 0.3 );
         legend->Draw("same");
      } 
 
      // redraw axes
      sig->Draw("sameaxis");
 
      // text for overflows
      Int_t    nbin = sig->GetNbinsX();
      Double_t dxu  = sig->GetBinWidth(0);
      Double_t dxo  = sig->GetBinWidth(nbin+1);
      TString uoflow = "";
      if (isRegression) {
         uoflow = Form( "U/O-flow: %.1f%% / %.1f%%", 
                        sig->GetBinContent(0)*dxu*100, sig->GetBinContent(nbin+1)*dxo*100 );
      }
      else {
         uoflow = Form( "U/O-flow (S,B): (%.1f, %.1f)%% / (%.1f, %.1f)%%", 
                        sig->GetBinContent(0)*dxu*100, bgd->GetBinContent(0)*dxu*100,
                        sig->GetBinContent(nbin+1)*dxo*100, bgd->GetBinContent(nbin+1)*dxo*100 );
      }
  
      TText* t = new TText( 0.98, 0.14, uoflow );
      t->SetNDC();
      t->SetTextSize( 0.040 );
      t->SetTextAngle( 90 );
      t->AppendPad();    
 
      // save canvas to file
      if (countPad%noPadPerCanv==0) {
         TString fname = Form( "%s/plots/%s_c%i",dataset.Data(), outfname.Data(), countCanvas );
         TMVAGlob::plot_logo();
         TMVAGlob::imgconv( canv, fname );
         createNewFig = kFALSE;
      }
      else {
         createNewFig = kTRUE;
      }
   }
   
   if (createNewFig) {
      TString fname = Form( "%s/plots/%s_c%i",dataset.Data(), outfname.Data(), countCanvas );
      TMVAGlob::plot_logo();
      TMVAGlob::imgconv( canv, fname );
      createNewFig = kFALSE;
   }
 
   return;
}