// input: - Input file (result from TMVA)
// - use of TMVA plotting TStyle
void mvas( TString fin = "TMVA.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 );
// 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 << flush;
TString hname = "MVA_" + methodTitle;
if (htype == ProbaType ) hname += "_Proba";
else if (htype == RarityType ) hname += "_Rarity";
TH1* sig = dynamic_cast<TH1*>(titDir->Get( hname + "_S" ));
TH1* bgd = dynamic_cast<TH1*>(titDir->Get( hname + "_B" ));
if (sig==0 || bgd==0) {
if (htype == MVAType)
cout << "mva distribution not available (this is normal for Cut classifier)" << endl;
else if(htype == ProbaType)
cout << "probability distribution not available (this is normal for Cut classifier)" << endl;
else if(htype == RarityType)
cout << "rarity distribution not available (this is normal for Cut classifier)" << endl;
else if(htype == CompareType)
cout << "overtraining check not available (this is normal for Cut classifier)" << endl;
else cout << endl;
}
else {
cout << endl;
// chop off useless stuff
sig->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data()) );
if (htype == ProbaType)
sig->SetTitle( Form("TMVA probability for classifier: %s", methodTitle.Data()) );
else if (htype == RarityType)
sig->SetTitle( Form("TMVA Rarity for classifier: %s", methodTitle.Data()) );
else if (htype == CompareType)
sig->SetTitle( Form("TMVA overtraining check for classifier: %s", methodTitle.Data()) );
// create new canvas
TString ctitle = ((htype == MVAType) ?
Form("TMVA response %s",methodTitle.Data()) :
(htype == ProbaType) ?
Form("TMVA probability %s",methodTitle.Data()) :
(htype == CompareType) ?
Form("TMVA comparison %s",methodTitle.Data()) :
Form("TMVA Rarity %s",methodTitle.Data()));
TString cname = ((htype == MVAType) ?
Form("output_%s",methodTitle.Data()) :
(htype == ProbaType) ?
Form("probability_%s",methodTitle.Data()) :
(htype == CompareType) ?
Form("comparison_%s",methodTitle.Data()) :
Form("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 = 4;
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 == CompareType) ? 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 == MVAType || htype == CompareType) ? " response" : "") );
if (htype == ProbaType ) frame->GetXaxis()->SetTitle( "Signal probability" );
else if (htype == RarityType ) frame->GetXaxis()->SetTitle( "Signal rarity" );
frame->GetYaxis()->SetTitle("Normalized");
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 );
legend->AddEntry(sig,TString("Signal") + ((htype == CompareType) ? " (test sample)" : ""), "F");
legend->AddEntry(bgd,TString("Background") + ((htype == CompareType) ? " (test sample)" : ""), "F");
legend->SetBorderSize(1);
legend->SetMargin( (htype == CompareType ? 0.2 : 0.3) );
legend->Draw("same");
// overlay signal and background histograms
sig->Draw("samehist");
bgd->Draw("samehist");
if (htype == CompareType) {
// if overtraining check, load additional histograms
TH1* sigOv = 0;
TH1* bgdOv = 0;
TString ovname = hname += "_Train";
sigOv = dynamic_cast<TH1*>(titDir->Get( ovname + "_S" ));
bgdOv = dynamic_cast<TH1*>(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");
}
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, 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 );
Double_t kolB = bgd->KolmogorovTest( bgdOv );
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 == MVAType) TMVAGlob::imgconv( c, Form("plots/mva_%s", methodTitle.Data()) );
else if (htype == ProbaType) TMVAGlob::imgconv( c, Form("plots/proba_%s", methodTitle.Data()) );
else if (htype == CompareType) TMVAGlob::imgconv( c, Form("plots/overtrain_%s", methodTitle.Data()) );
else TMVAGlob::imgconv( c, Form("plots/rarity_%s", methodTitle.Data()) );
}
countCanvas++;
}
}
}
}
void DrawMLPoutputMovie( TFile* file, const TString& methodType, const TString& methodTitle )
{
gROOT->SetBatch( 1 );
// define Canvas layout here!
const Int_t width = 600; // size of canvas
// this defines how many canvases we need
TCanvas* c = 0;
Float_t nrms = 4;
Float_t xmin = -1.2;
Float_t xmax = 1.2;
Float_t ymin = 0;
Float_t ymax = 0;
Float_t maxMult = 6.0;
Int_t countCanvas = 0;
Bool_t first = kTRUE;
TString dirname = methodType + "/" + methodTitle + "/" + "EpochMonitoring";
TDirectory *epochDir = (TDirectory*)file->Get( dirname );
if (!epochDir) {
cout << "Big troubles: could not find directory \"" << dirname << "\"" << endl;
exit(1);
}
// now read all evolution histograms
TIter keyItTit(epochDir->GetListOfKeys());
TKey *titkeyTit;
while ((titkeyTit = (TKey*)keyItTit())) {
if (!gROOT->GetClass(titkeyTit->GetClassName())->InheritsFrom("TH1F")) continue;
TString name = titkeyTit->GetName();
if (!name.BeginsWith("convergencetest___")) continue;
if (!name.Contains("_train_")) continue; // only for training so far
if (name.EndsWith( "_B")) continue;
// must be signal histogram
if (!name.EndsWith( "_S")) {
cout << "Big troubles with histogram: " << name << " -> should end with _S" << endl;
exit(1);
}
// create canvas
countCanvas++;
TString ctitle = Form("TMVA response %s",methodTitle.Data());
c = new TCanvas( Form("canvas%d", countCanvas), ctitle, 0, 0, width, (Int_t)width*0.78 );
TH1F* sig = (TH1F*)titkeyTit->ReadObj();
sig->SetTitle( Form("TMVA response for classifier: %s", methodTitle.Data()) );
TString dataType = (name.Contains("_train_") ? "(training sample)" : "(test sample)");
// find background
TString nbn = sig->GetName(); nbn[nbn.Length()-1] = 'B';
TH1F* bgd = dynamic_cast<TH1F*>(epochDir->Get( nbn ));
if (bgd == 0) {
cout << "Big troubles with histogram: " << bgd << " -> cannot find!" << endl;
exit(1);
}
cout << "sig = " << sig->GetName() << endl;
cout << "bgd = " << bgd->GetName() << endl;
// set the histogram style
TMVAGlob::SetSignalAndBackgroundStyle( sig, bgd );
// normalise both signal and background
TMVAGlob::NormalizeHists( sig, bgd );
// set only first time, then same for all plots
if (first) {
if (xmin == 0 && xmax == 0) {
xmin = TMath::Max( TMath::Min(sig->GetMean() - nrms*sig->GetRMS(),
bgd->GetMean() - nrms*bgd->GetRMS() ),
sig->GetXaxis()->GetXmin() );
xmax = TMath::Min( TMath::Max(sig->GetMean() + nrms*sig->GetRMS(),
bgd->GetMean() + nrms*bgd->GetRMS() ),
sig->GetXaxis()->GetXmax() );
}
ymin = 0;
ymax = TMath::Max( sig->GetMaximum(), bgd->GetMaximum() )*maxMult;
first = kFALSE;
}
// build a frame
Int_t nb = 100;
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 + " response" );
frame->GetYaxis()->SetTitle("(1/N) dN^{ }/^{ }dx");
TMVAGlob::SetFrameStyle( frame );
// find epoch number (4th token)
TObjArray* tokens = name.Tokenize("_");
TString es = ((TObjString*)tokens->At(4))->GetString();
if (!es.IsFloat()) {
cout << "Big troubles in epoch parsing: \"" << es << "\" is not float" << endl;
exit(1);
}
Int_t epoch = es.Atoi();
// 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() + 0.5, 1 - c->GetTopMargin() );
legend->SetFillStyle( 1 );
legend->AddEntry(sig,TString("Signal ") + dataType, "F");
legend->AddEntry(bgd,TString("Background ") + dataType, "F");
legend->SetBorderSize(1);
legend->SetMargin( 0.15 );
legend->Draw("same");
TText* t = new TText();
t->SetTextSize( 0.04 );
t->SetTextColor( 1 );
t->SetTextAlign( 31 );
t->DrawTextNDC( 1 - c->GetRightMargin(), 1 - c->GetTopMargin() + 0.015, Form( "Epoch: %i", epoch) );
// overlay signal and background histograms
sig->Draw("samehist");
bgd->Draw("samehist");
// save to file
TString dirname = "movieplots";
TString foutname = dirname + "/" + name;
foutname.Resize( foutname.Length()-2 );
foutname.ReplaceAll("convergencetest___","");
foutname += ".gif";
cout << "storing file: " << foutname << endl;
c->Update();
c->Print(foutname);
}
}