mvasMulticlass.C

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#include "TLegend.h"
#include "TText.h"
#include "TH2.h"

#include "tmvaglob.C"

// this macro plots the resulting MVA distributions (Signal and
// Background overlayed) of different MVA methods run in TMVA
// (e.g. running TMVAnalysis.C).

enum HistType { MVAType = 0, CompareType = 1 };

// input: - Input file (result from TMVA)
//        - use of TMVA plotting TStyle
void mvasMulticlass( TString fin = "TMVAMulticlass.root", HistType htype = MVAType, Bool_t useTMVAStyle = kTRUE )
{
   // set style and remove existing canvas'
   TMVAGlob::Initialize( useTMVAStyle );

   // switches
   const Bool_t Save_Images = kTRUE;

   // checks if file with name "fin" is already open, and if not opens one
   TFile* file = TMVAGlob::OpenFile( fin );  

   TDirectory* tempdir = (TDirectory*)file->Get("InputVariables_Id" );
   std::vector<TString> classnames(TMVAGlob::GetClassNames(tempdir));

   // define Canvas layout here!
   Int_t xPad = 1; // no of plots in x
   Int_t yPad = 1; // no of plots in y
   Int_t noPad = xPad * yPad ; 
   const Int_t width = 600;   // size of canvas

   // this defines how many canvases we need
   TCanvas *c = 0;

   // counter variables
   Int_t countCanvas = 0;

   // search for the right histograms in full list of keys
   TIter next(file->GetListOfKeys());
   TKey *key(0);   
   while ((key = (TKey*)next())) {

      if (!TString(key->GetName()).BeginsWith("Method_")) continue;
      if (!gROOT->GetClass(key->GetClassName())->InheritsFrom("TDirectory")) continue;

      TString methodName;
      TMVAGlob::GetMethodName(methodName,key);

      TDirectory* mDir = (TDirectory*)key->ReadObj();

      TIter keyIt(mDir->GetListOfKeys());
      TKey *titkey;
      while ((titkey = (TKey*)keyIt())) {

         if (!gROOT->GetClass(titkey->GetClassName())->InheritsFrom("TDirectory")) continue;

         TDirectory *titDir = (TDirectory *)titkey->ReadObj();
         TString methodTitle;
         TMVAGlob::GetMethodTitle(methodTitle,titDir);

         cout << "--- Found directory for method: " << methodName << "::" << methodTitle << endl;
         TString hname = "MVA_" + methodTitle;
          for(Int_t icls = 0; icls < classnames.size(); ++icls){
         TObjArray hists;

         std::vector<TString>::iterator classiter = classnames.begin();
         for(; classiter!=classnames.end(); ++classiter){
            TString name(hname+"_Test_"+ classnames.at(icls)
                         + "_prob_for_" + *classiter);
            TH1 *hist = (TH1*)titDir->Get(name);
            if (hist==0){
               cout << ":\t mva distribution not available (this is normal for Cut classifier)" << endl;
               continue;
            }
            hists.Add(hist);
         }
         
       
         // chop off useless stuff
         ((TH1*)hists.First())->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data() ));
           
         // create new canvas
         //cout << "Create canvas..." << endl;
         TString ctitle = ((htype == MVAType) ? 
                           Form("TMVA response for class %s %s", classnames.at(icls).Data(),methodTitle.Data()) :                
                           Form("TMVA comparison for class %s %s", classnames.at(icls).Data(),methodTitle.Data())) ;
         
         c = new TCanvas( Form("canvas%d", countCanvas+1), ctitle, 
                          countCanvas*50+200, countCanvas*20, width, (Int_t)width*0.78 ); 
    
         // set the histogram style
         //cout << "Set histogram style..." << endl;
         TMVAGlob::SetMultiClassStyle( &hists );
         
         // normalise all histograms and find maximum
         Float_t histmax = -1;
         for(Int_t i=0; i<hists.GetEntriesFast(); ++i){
            TMVAGlob::NormalizeHist((TH1*)hists[i] );
            if(((TH1*)hists[i])->GetMaximum() > histmax)
               histmax = ((TH1*)hists[i])->GetMaximum();
         }
         
         // frame limits (between 0 and 1 per definition)
         Float_t xmin = 0;
         Float_t xmax = 1;
         Float_t ymin = 0;
         Float_t maxMult = (htype == CompareType) ? 1.3 : 1.2;
         Float_t ymax = histmax*maxMult; 
         // build a frame
         Int_t nb = 500;
         TString hFrameName(TString("frame") + methodTitle);
         TObject *o = gROOT->FindObject(hFrameName);
         if(o) delete o;
         TH2F* frame = new TH2F( hFrameName, ((TH1*)hists.First())->GetTitle(), 
                                 nb, xmin, xmax, nb, ymin, ymax );
         frame->GetXaxis()->SetTitle( methodTitle + " response for "+classnames.at(icls));
          frame->GetYaxis()->SetTitle("(1/N) dN^{ }/^{ }dx");
         TMVAGlob::SetFrameStyle( frame );
   
         // eventually: draw the frame
         frame->Draw();  
    
         c->GetPad(0)->SetLeftMargin( 0.105 );
         frame->GetYaxis()->SetTitleOffset( 1.2 );

         // Draw legend               
         TLegend *legend= new TLegend( c->GetLeftMargin(), 1 - c->GetTopMargin() - 0.12, 
                                       c->GetLeftMargin() + (htype == CompareType ? 0.40 : 0.3), 1 - c->GetTopMargin() );
         legend->SetFillStyle( 1 );
         classiter = classnames.begin();
         
         for(Int_t i=0; i<hists.GetEntriesFast(); ++i, ++classiter){
            legend->AddEntry(((TH1*)hists[i]),*classiter,"F");
         }
         
         legend->SetBorderSize(1);
         legend->SetMargin( 0.3 );
         legend->Draw("same");
         
         
         for(Int_t i=0; i<hists.GetEntriesFast(); ++i){

            ((TH1*)hists[i])->Draw("histsame");
            TString ytit = TString("(1/N) ") + ((TH1*)hists[i])->GetYaxis()->GetTitle();
            ((TH1*)hists[i])->GetYaxis()->SetTitle( ytit ); // histograms are normalised
      
         }
       
         
         if (htype == CompareType) {
            
            TObjArray othists; 
            // if overtraining check, load additional histograms
            classiter = classnames.begin();
            for(; classiter!=classnames.end(); ++classiter){
               TString name(hname+"_Train_"+ classnames.at(icls)
                            + "_prob_for_" + *classiter);
               TH1 *hist = (TH1*)titDir->Get(name);
               if (hist==0){
                  cout << ":\t comparison histogram for overtraining check not available!" << endl;
                  continue;
               }
               othists.Add(hist);
            }
            
            TLegend *legend2= new TLegend( 1 - c->GetRightMargin() - 0.42, 1 - c->GetTopMargin() - 0.12,
                                           1 - c->GetRightMargin(), 1 - c->GetTopMargin() );
            legend2->SetFillStyle( 1 );
            legend2->SetBorderSize(1);
            
            classiter = classnames.begin();
            for(Int_t i=0; i<othists.GetEntriesFast(); ++i, ++classiter){
               legend2->AddEntry(((TH1*)othists[i]),*classiter+" (training sample)","P");
            }
            legend2->SetMargin( 0.1 );
            legend2->Draw("same");
            
            // normalise all histograms and get maximum
            for(Int_t i=0; i<othists.GetEntriesFast(); ++i){
               TMVAGlob::NormalizeHist((TH1*)othists[i] );
               if(((TH1*)othists[i])->GetMaximum() > histmax)
                  histmax = ((TH1*)othists[i])->GetMaximum();
            }

            TMVAGlob::SetMultiClassStyle( &othists );
            for(Int_t i=0; i<othists.GetEntriesFast(); ++i){
               Int_t col = ((TH1*)hists[i])->GetLineColor();
               ((TH1*)othists[i])->SetMarkerSize( 0.7 );
               ((TH1*)othists[i])->SetMarkerStyle( 20 );
               ((TH1*)othists[i])->SetMarkerColor( col );
               ((TH1*)othists[i])->SetLineWidth( 1 );
               ((TH1*)othists[i])->Draw("e1same");
            }
            
            ymax = histmax*maxMult;
            frame->GetYaxis()->SetLimits( 0, ymax );
      
            // for better visibility, plot thinner lines
            TMVAGlob::SetMultiClassStyle( &othists );
            for(Int_t i=0; i<hists.GetEntriesFast(); ++i){
                ((TH1*)hists[i])->SetLineWidth( 1 );
            }
            
            
            // perform K-S test
            
            cout << "--- Perform Kolmogorov-Smirnov tests" << endl;
            cout << "--- Goodness of consistency for class " << classnames.at(icls)<< endl;
            //TString probatext("Kolmogorov-Smirnov test: ");
            for(Int_t j=0; j<othists.GetEntriesFast(); ++j){
               Float_t kol = ((TH1*)hists[j])->KolmogorovTest(((TH1*)othists[j]));
               cout <<  classnames.at(j) << ": " << kol  << endl;
               //probatext.Append(classnames.at(j)+Form(" %.3f ",kol));
            }
            
           
           
            //TText* tt = new TText( 0.12, 0.74, probatext );
            //tt->SetNDC(); tt->SetTextSize( 0.032 ); tt->AppendPad();
            
         }
         
         
         // redraw axes
         frame->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 = 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( 0.975, 0.115, uoflow );
         //t->SetNDC();
         //t->SetTextSize( 0.030 );
         //t->SetTextAngle( 90 );
         //t->AppendPad();    
   
         // update canvas
         c->Update();

         // save canvas to file

         TMVAGlob::plot_logo(1.058);
         if (Save_Images) {
            if      (htype == MVAType)     TMVAGlob::imgconv( c, Form("plots/mva_%s_%s",classnames.at(icls).Data(), methodTitle.Data()) );
            else if      (htype == CompareType)     TMVAGlob::imgconv( c, Form("plots/overtrain_%s_%s",classnames.at(icls).Data(), methodTitle.Data()) );

         }
         countCanvas++;
         }
      }
      cout << "";
   }
}

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