#include "TMVA/mvas.h" #include "TMVA/Types.h" #include "TLegend.h" #include "TText.h" #include "TH2.h" // this macro plots the resulting MVA distributions (Signal and // Background overlayed) of different MVA methods run in TMVA // (e.g. running TMVAnalysis.C). // input: - Input file (result from TMVA) // - use of TMVA plotting TStyle void TMVA::mvas(TString dataset, TString fin, HistType htype, Bool_t useTMVAStyle ) { // 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 ); // define Canvas layout here! 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->GetDirectory(dataset.Data())->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); std::cout << "--- Found directory for method: " << methodName << "::" << methodTitle << std::flush; TString hname = "MVA_" + methodTitle; if (htype == kProbaType ) hname += "_Proba"; else if (htype == kRarityType ) hname += "_Rarity"; TH1* sig = dynamic_cast(titDir->Get( hname + "_S" )); TH1* bgd = dynamic_cast(titDir->Get( hname + "_B" )); if (sig==0 || bgd==0) { if (htype == kMVAType) cout << ":\t mva distribution not available (this is normal for Cut classifier)" << endl; else if(htype == kProbaType) cout << ":\t probability distribution not available" << endl; else if(htype == kRarityType) cout << ":\t rarity distribution not available" << endl; else if(htype == kCompareType) cout << ":\t overtraining check not available" << endl; else cout << endl; continue; } cout << " containing " << hname << "_S/_B" << endl; // chop off useless stuff sig->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data()) ); if (htype == kProbaType) sig->SetTitle( Form("TMVA probability for classifier: %s", methodTitle.Data()) ); else if (htype == kRarityType) sig->SetTitle( Form("TMVA Rarity for classifier: %s", methodTitle.Data()) ); else if (htype == kCompareType) sig->SetTitle( Form("TMVA overtraining check for classifier: %s", methodTitle.Data()) ); // create new canvas TString ctitle = ((htype == kMVAType) ? Form("TMVA response %s",methodTitle.Data()) : (htype == kProbaType) ? Form("TMVA probability %s",methodTitle.Data()) : (htype == kCompareType) ? Form("TMVA comparison %s",methodTitle.Data()) : Form("TMVA Rarity %s",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 TMVAGlob::SetSignalAndBackgroundStyle( sig, bgd ); // normalise both signal and background TMVAGlob::NormalizeHists( sig, bgd ); // frame limits (choose judicuous x range) Float_t nrms = 10; cout << "--- Mean and RMS (S): " << sig->GetMean() << ", " << sig->GetRMS() << endl; cout << "--- Mean and RMS (B): " << bgd->GetMean() << ", " << bgd->GetRMS() << endl; Float_t xmin = TMath::Max( TMath::Min(sig->GetMean() - nrms*sig->GetRMS(), bgd->GetMean() - nrms*bgd->GetRMS() ), sig->GetXaxis()->GetXmin() ); Float_t xmax = TMath::Min( TMath::Max(sig->GetMean() + nrms*sig->GetRMS(), bgd->GetMean() + nrms*bgd->GetRMS() ), sig->GetXaxis()->GetXmax() ); Float_t ymin = 0; Float_t maxMult = (htype == kCompareType) ? 1.3 : 1.2; Float_t ymax = TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*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, sig->GetTitle(), nb, xmin, xmax, nb, ymin, ymax ); frame->GetXaxis()->SetTitle( methodTitle + ((htype == kMVAType || htype == kCompareType) ? " response" : "") ); if (htype == kProbaType ) frame->GetXaxis()->SetTitle( "Signal probability" ); else if (htype == kRarityType ) frame->GetXaxis()->SetTitle( "Signal rarity" ); 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 == kCompareType ? 0.40 : 0.3), 1 - c->GetTopMargin() ); legend->SetFillStyle( 1 ); legend->AddEntry(sig,TString("Signal") + ((htype == kCompareType) ? " (test sample)" : ""), "F"); legend->AddEntry(bgd,TString("Background") + ((htype == kCompareType) ? " (test sample)" : ""), "F"); legend->SetBorderSize(1); legend->SetMargin( (htype == kCompareType ? 0.2 : 0.3) ); legend->Draw("same"); // overlay signal and background histograms sig->Draw("samehist"); bgd->Draw("samehist"); if (htype == kCompareType) { // if overtraining check, load additional histograms TH1* sigOv = 0; TH1* bgdOv = 0; TString ovname = hname += "_Train"; sigOv = dynamic_cast(titDir->Get( ovname + "_S" )); bgdOv = dynamic_cast(titDir->Get( ovname + "_B" )); if (sigOv == 0 || bgdOv == 0) { cout << "+++ Problem in \"mvas.C\": overtraining check histograms do not exist" << endl; } else { cout << "--- Found comparison histograms for overtraining check" << endl; 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); legend2->AddEntry(sigOv,"Signal (training sample)","P"); legend2->AddEntry(bgdOv,"Background (training sample)","P"); legend2->SetMargin( 0.1 ); legend2->Draw("same"); } // normalise both signal and background TMVAGlob::NormalizeHists( sigOv, bgdOv ); Int_t col = sig->GetLineColor(); sigOv->SetMarkerColor( col ); sigOv->SetMarkerSize( 0.7 ); sigOv->SetMarkerStyle( 20 ); sigOv->SetLineWidth( 1 ); sigOv->SetLineColor( col ); sigOv->Draw("e1same"); col = bgd->GetLineColor(); bgdOv->SetMarkerColor( col ); bgdOv->SetMarkerSize( 0.7 ); bgdOv->SetMarkerStyle( 20 ); bgdOv->SetLineWidth( 1 ); bgdOv->SetLineColor( col ); bgdOv->Draw("e1same"); ymax = TMath::Max( ymax, float(TMath::Max( sigOv->GetMaximum(), bgdOv->GetMaximum() )*maxMult )); frame->GetYaxis()->SetLimits( 0, ymax ); // for better visibility, plot thinner lines sig->SetLineWidth( 1 ); bgd->SetLineWidth( 1 ); // perform K-S test cout << "--- Perform Kolmogorov-Smirnov tests" << endl; Double_t kolS = sig->KolmogorovTest( sigOv, "X" ); Double_t kolB = bgd->KolmogorovTest( bgdOv, "X" ); cout << "--- Goodness of signal (background) consistency: " << kolS << " (" << kolB << ")" << endl; TString probatext = Form( "Kolmogorov-Smirnov test: signal (background) probability = %5.3g (%5.3g)", kolS, kolB ); 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 == kMVAType) TMVAGlob::imgconv( c, Form("%s/plots/mva_%s",dataset.Data(), methodTitle.Data()) ); else if (htype == kProbaType) TMVAGlob::imgconv( c, Form("%s/plots/proba_%s",dataset.Data(), methodTitle.Data()) ); else if (htype == kCompareType) TMVAGlob::imgconv( c, Form("%s/plots/overtrain_%s",dataset.Data(), methodTitle.Data()) ); else TMVAGlob::imgconv( c, Form("%s/plots/rarity_%s",dataset.Data(), methodTitle.Data()) ); } countCanvas++; } cout << ""; } }