137#pragma warning ( disable : 4355 )
158 fMultiGraph =
nullptr;
172 std::cerr << kERROR <<
"IPythonInteractive::Init: already initialized..." << std::endl;
176 for(
auto& title : graphTitles){
177 fGraphs.push_back(
new TGraph() );
178 fGraphs.back()->SetTitle(title);
179 fGraphs.back()->SetName(title);
180 fGraphs.back()->SetFillColor(color);
181 fGraphs.back()->SetLineColor(color);
182 fGraphs.back()->SetMarkerColor(color);
183 fMultiGraph->Add(fGraphs.back());
195 for(
Int_t i=0; i<fNumGraphs; i++){
209 fGraphs[0]->Set(fIndex+1);
210 fGraphs[1]->Set(fIndex+1);
211 fGraphs[0]->SetPoint(fIndex,
x,
y1);
212 fGraphs[1]->SetPoint(fIndex,
x,
y2);
225 for(
Int_t i=0; i<fNumGraphs;i++){
226 fGraphs[i]->Set(fIndex+1);
227 fGraphs[i]->SetPoint(fIndex, dat[0], dat[i+1]);
247 fAnalysisType (
Types::kNoAnalysisType ),
248 fRegressionReturnVal ( 0 ),
249 fMulticlassReturnVal ( 0 ),
250 fDataSetInfo ( dsi ),
251 fSignalReferenceCut ( 0.5 ),
252 fSignalReferenceCutOrientation( 1. ),
253 fVariableTransformType (
Types::kSignal ),
254 fJobName ( jobName ),
255 fMethodName ( methodTitle ),
256 fMethodType ( methodType ),
260 fConstructedFromWeightFile (
kFALSE ),
262 fMethodBaseDir ( 0 ),
265 fModelPersistence (
kTRUE),
276 fSplTrainEffBvsS ( 0 ),
277 fVarTransformString (
"None" ),
278 fTransformationPointer ( 0 ),
279 fTransformation ( dsi, methodTitle ),
281 fVerbosityLevelString (
"Default" ),
284 fIgnoreNegWeightsInTraining(
kFALSE ),
286 fBackgroundClass ( 0 ),
311 fAnalysisType (
Types::kNoAnalysisType ),
312 fRegressionReturnVal ( 0 ),
313 fMulticlassReturnVal ( 0 ),
314 fDataSetInfo ( dsi ),
315 fSignalReferenceCut ( 0.5 ),
316 fVariableTransformType (
Types::kSignal ),
318 fMethodName (
"MethodBase" ),
319 fMethodType ( methodType ),
321 fTMVATrainingVersion ( 0 ),
322 fROOTTrainingVersion ( 0 ),
323 fConstructedFromWeightFile (
kTRUE ),
325 fMethodBaseDir ( 0 ),
328 fModelPersistence (
kTRUE),
329 fWeightFile ( weightFile ),
339 fSplTrainEffBvsS ( 0 ),
340 fVarTransformString (
"None" ),
341 fTransformationPointer ( 0 ),
342 fTransformation ( dsi,
"" ),
344 fVerbosityLevelString (
"Default" ),
347 fIgnoreNegWeightsInTraining(
kFALSE ),
349 fBackgroundClass ( 0 ),
367 if (!fSetupCompleted) Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Calling destructor of method which got never setup" <<
Endl;
370 if (fInputVars != 0) { fInputVars->clear();
delete fInputVars; }
371 if (fRanking != 0)
delete fRanking;
374 if (fDefaultPDF!= 0) {
delete fDefaultPDF; fDefaultPDF = 0; }
375 if (fMVAPdfS != 0) {
delete fMVAPdfS; fMVAPdfS = 0; }
376 if (fMVAPdfB != 0) {
delete fMVAPdfB; fMVAPdfB = 0; }
379 if (fSplS) {
delete fSplS; fSplS = 0; }
380 if (fSplB) {
delete fSplB; fSplB = 0; }
381 if (fSpleffBvsS) {
delete fSpleffBvsS; fSpleffBvsS = 0; }
382 if (fSplRefS) {
delete fSplRefS; fSplRefS = 0; }
383 if (fSplRefB) {
delete fSplRefB; fSplRefB = 0; }
384 if (fSplTrainRefS) {
delete fSplTrainRefS; fSplTrainRefS = 0; }
385 if (fSplTrainRefB) {
delete fSplTrainRefB; fSplTrainRefB = 0; }
386 if (fSplTrainEffBvsS) {
delete fSplTrainEffBvsS; fSplTrainEffBvsS = 0; }
388 for (
size_t i = 0; i < fEventCollections.size(); i++ ) {
389 if (fEventCollections.at(i)) {
390 for (std::vector<Event*>::const_iterator it = fEventCollections.at(i)->begin();
391 it != fEventCollections.at(i)->end(); ++it) {
394 delete fEventCollections.at(i);
395 fEventCollections.at(i) =
nullptr;
399 if (fRegressionReturnVal)
delete fRegressionReturnVal;
400 if (fMulticlassReturnVal)
delete fMulticlassReturnVal;
410 if (fSetupCompleted) Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Calling SetupMethod for the second time" <<
Endl;
412 DeclareBaseOptions();
415 fSetupCompleted =
kTRUE;
425 ProcessBaseOptions();
435 CheckForUnusedOptions();
443 SetConfigDescription(
"Configuration options for classifier architecture and tuning" );
451 fSplTrainEffBvsS = 0;
458 fTxtWeightsOnly =
kTRUE;
468 fInputVars =
new std::vector<TString>;
469 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
470 fInputVars->push_back(DataInfo().GetVariableInfo(ivar).GetLabel());
472 fRegressionReturnVal = 0;
473 fMulticlassReturnVal = 0;
475 fEventCollections.resize( 2 );
476 fEventCollections.at(0) = 0;
477 fEventCollections.at(1) = 0;
480 if (DataInfo().GetClassInfo(
"Signal") != 0) {
481 fSignalClass = DataInfo().GetClassInfo(
"Signal")->GetNumber();
483 if (DataInfo().GetClassInfo(
"Background") != 0) {
484 fBackgroundClass = DataInfo().GetClassInfo(
"Background")->GetNumber();
487 SetConfigDescription(
"Configuration options for MVA method" );
488 SetConfigName(
TString(
"Method") + GetMethodTypeName() );
511 DeclareOptionRef( fVerbose,
"V",
"Verbose output (short form of \"VerbosityLevel\" below - overrides the latter one)" );
513 DeclareOptionRef( fVerbosityLevelString=
"Default",
"VerbosityLevel",
"Verbosity level" );
514 AddPreDefVal(
TString(
"Default") );
515 AddPreDefVal(
TString(
"Debug") );
516 AddPreDefVal(
TString(
"Verbose") );
517 AddPreDefVal(
TString(
"Info") );
518 AddPreDefVal(
TString(
"Warning") );
519 AddPreDefVal(
TString(
"Error") );
520 AddPreDefVal(
TString(
"Fatal") );
524 fTxtWeightsOnly =
kTRUE;
527 DeclareOptionRef( fVarTransformString,
"VarTransform",
"List of variable transformations performed before training, e.g., \"D_Background,P_Signal,G,N_AllClasses\" for: \"Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)\"" );
529 DeclareOptionRef( fHelp,
"H",
"Print method-specific help message" );
531 DeclareOptionRef( fHasMVAPdfs,
"CreateMVAPdfs",
"Create PDFs for classifier outputs (signal and background)" );
533 DeclareOptionRef( fIgnoreNegWeightsInTraining,
"IgnoreNegWeightsInTraining",
534 "Events with negative weights are ignored in the training (but are included for testing and performance evaluation)" );
546 fDefaultPDF =
new PDF(
TString(GetName())+
"_PDF", GetOptions(),
"MVAPdf" );
547 fDefaultPDF->DeclareOptions();
548 fDefaultPDF->ParseOptions();
549 fDefaultPDF->ProcessOptions();
550 fMVAPdfB =
new PDF(
TString(GetName())+
"_PDFBkg", fDefaultPDF->GetOptions(),
"MVAPdfBkg", fDefaultPDF );
551 fMVAPdfB->DeclareOptions();
552 fMVAPdfB->ParseOptions();
553 fMVAPdfB->ProcessOptions();
554 fMVAPdfS =
new PDF(
TString(GetName())+
"_PDFSig", fMVAPdfB->GetOptions(),
"MVAPdfSig", fDefaultPDF );
555 fMVAPdfS->DeclareOptions();
556 fMVAPdfS->ParseOptions();
557 fMVAPdfS->ProcessOptions();
560 SetOptions( fMVAPdfS->GetOptions() );
565 GetTransformationHandler(),
569 if (fDefaultPDF!= 0) {
delete fDefaultPDF; fDefaultPDF = 0; }
570 if (fMVAPdfS != 0) {
delete fMVAPdfS; fMVAPdfS = 0; }
571 if (fMVAPdfB != 0) {
delete fMVAPdfB; fMVAPdfB = 0; }
575 fVerbosityLevelString =
TString(
"Verbose");
576 Log().SetMinType( kVERBOSE );
578 else if (fVerbosityLevelString ==
"Debug" ) Log().SetMinType( kDEBUG );
579 else if (fVerbosityLevelString ==
"Verbose" ) Log().SetMinType( kVERBOSE );
580 else if (fVerbosityLevelString ==
"Info" ) Log().SetMinType( kINFO );
581 else if (fVerbosityLevelString ==
"Warning" ) Log().SetMinType( kWARNING );
582 else if (fVerbosityLevelString ==
"Error" ) Log().SetMinType( kERROR );
583 else if (fVerbosityLevelString ==
"Fatal" ) Log().SetMinType( kFATAL );
584 else if (fVerbosityLevelString !=
"Default" ) {
585 Log() << kFATAL <<
"<ProcessOptions> Verbosity level type '"
586 << fVerbosityLevelString <<
"' unknown." <<
Endl;
598 DeclareOptionRef( fNormalise=
kFALSE,
"Normalise",
"Normalise input variables" );
599 DeclareOptionRef( fUseDecorr=
kFALSE,
"D",
"Use-decorrelated-variables flag" );
600 DeclareOptionRef( fVariableTransformTypeString=
"Signal",
"VarTransformType",
601 "Use signal or background events to derive for variable transformation (the transformation is applied on both types of, course)" );
602 AddPreDefVal(
TString(
"Signal") );
603 AddPreDefVal(
TString(
"Background") );
604 DeclareOptionRef( fTxtWeightsOnly=
kTRUE,
"TxtWeightFilesOnly",
"If True: write all training results (weights) as text files (False: some are written in ROOT format)" );
614 DeclareOptionRef( fNbinsMVAPdf = 60,
"NbinsMVAPdf",
"Number of bins used for the PDFs of classifier outputs" );
615 DeclareOptionRef( fNsmoothMVAPdf = 2,
"NsmoothMVAPdf",
"Number of smoothing iterations for classifier PDFs" );
629 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Parameter optimization is not yet implemented for method "
630 << GetName() <<
Endl;
631 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Currently we need to set hardcoded which parameter is tuned in which ranges"<<
Endl;
633 std::map<TString,Double_t> tunedParameters;
634 tunedParameters.size();
635 return tunedParameters;
656 if (Help()) PrintHelpMessage();
659 if(!IsSilentFile()) BaseDir()->cd();
663 GetTransformationHandler().CalcTransformations(Data()->GetEventCollection());
667 <<
"Begin training" <<
Endl;
668 Long64_t nEvents = Data()->GetNEvents();
669 Timer traintimer( nEvents, GetName(),
kTRUE );
672 <<
"\tEnd of training " <<
Endl;
675 <<
"Elapsed time for training with " << nEvents <<
" events: "
679 <<
"\tCreate MVA output for ";
682 if (DoMulticlass()) {
683 Log() <<
Form(
"[%s] : ",DataInfo().GetName())<<
"Multiclass classification on training sample" <<
Endl;
686 else if (!DoRegression()) {
688 Log() <<
Form(
"[%s] : ",DataInfo().GetName())<<
"classification on training sample" <<
Endl;
697 Log() <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"regression on training sample" <<
Endl;
701 Log() <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Create PDFs" <<
Endl;
708 if (fModelPersistence ) WriteStateToFile();
711 if ((!DoRegression()) && (fModelPersistence)) MakeClass();
718 WriteMonitoringHistosToFile();
726 if (!DoRegression()) Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Trying to use GetRegressionDeviation() with a classification job" <<
Endl;
727 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Create results for " << (
type==
Types::kTraining?
"training":
"testing") <<
Endl;
729 bool truncate =
false;
730 TH1F*
h1 = regRes->QuadraticDeviation( tgtNum , truncate, 1.);
735 TH1F* h2 = regRes->QuadraticDeviation( tgtNum , truncate, yq[0]);
736 stddev90Percent = sqrt(h2->GetMean());
746 Data()->SetCurrentType(
type);
748 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Create results for " << (
type==
Types::kTraining?
"training":
"testing") <<
Endl;
752 Long64_t nEvents = Data()->GetNEvents();
756 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"Evaluation of " << GetMethodName() <<
" on "
759 regRes->Resize( nEvents );
764 Int_t totalProgressDraws = 100;
765 Int_t drawProgressEvery = 1;
766 if(nEvents >= totalProgressDraws) drawProgressEvery = nEvents/totalProgressDraws;
768 for (
Int_t ievt=0; ievt<nEvents; ievt++) {
770 Data()->SetCurrentEvent(ievt);
771 std::vector< Float_t > vals = GetRegressionValues();
772 regRes->SetValue( vals, ievt );
775 if(ievt % drawProgressEvery == 0 || ievt==nEvents-1) timer.
DrawProgressBar( ievt );
778 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())
779 <<
"Elapsed time for evaluation of " << nEvents <<
" events: "
786 TString histNamePrefix(GetTestvarName());
788 regRes->CreateDeviationHistograms( histNamePrefix );
796 Data()->SetCurrentType(
type);
798 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Create results for " << (
type==
Types::kTraining?
"training":
"testing") <<
Endl;
801 if (!resMulticlass) Log() << kFATAL<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"unable to create pointer in AddMulticlassOutput, exiting."<<
Endl;
803 Long64_t nEvents = Data()->GetNEvents();
808 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Multiclass evaluation of " << GetMethodName() <<
" on "
811 resMulticlass->Resize( nEvents );
813 for (
Int_t ievt=0; ievt<nEvents; ievt++) {
814 Data()->SetCurrentEvent(ievt);
815 std::vector< Float_t > vals = GetMulticlassValues();
816 resMulticlass->SetValue( vals, ievt );
820 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())
821 <<
"Elapsed time for evaluation of " << nEvents <<
" events: "
828 TString histNamePrefix(GetTestvarName());
831 resMulticlass->CreateMulticlassHistos( histNamePrefix, fNbinsMVAoutput, fNbinsH );
832 resMulticlass->CreateMulticlassPerformanceHistos(histNamePrefix);
839 if (errUpper) *errUpper=-1;
846 Double_t val = GetMvaValue(err, errUpper);
856 return GetMvaValue()*GetSignalReferenceCutOrientation() > GetSignalReferenceCut()*GetSignalReferenceCutOrientation() ?
kTRUE :
kFALSE;
863 return mvaVal*GetSignalReferenceCutOrientation() > GetSignalReferenceCut()*GetSignalReferenceCutOrientation() ?
kTRUE :
kFALSE;
871 Data()->SetCurrentType(
type);
876 Long64_t nEvents = Data()->GetNEvents();
881 std::vector<Double_t> mvaValues = GetMvaValues(0, nEvents,
true);
888 for (
Int_t ievt = 0; ievt < nEvents; ievt++) {
891 auto ev = Data()->GetEvent(ievt);
892 clRes->
SetValue(mvaValues[ievt], ievt, DataInfo().IsSignal(ev));
901 Long64_t nEvents = Data()->GetNEvents();
902 if (firstEvt > lastEvt || lastEvt > nEvents) lastEvt = nEvents;
903 if (firstEvt < 0) firstEvt = 0;
904 std::vector<Double_t> values(lastEvt-firstEvt);
906 nEvents = values.size();
912 Log() << kHEADER <<
Form(
"[%s] : ",DataInfo().GetName())
913 <<
"Evaluation of " << GetMethodName() <<
" on "
915 <<
" sample (" << nEvents <<
" events)" <<
Endl;
917 for (
Int_t ievt=firstEvt; ievt<lastEvt; ievt++) {
918 Data()->SetCurrentEvent(ievt);
919 values[ievt] = GetMvaValue();
924 if (modulo <= 0 ) modulo = 1;
930 <<
"Elapsed time for evaluation of " << nEvents <<
" events: "
943 auto result = GetMvaValues(firstEvt, lastEvt, logProgress);
953 Data()->SetCurrentType(
type);
958 Long64_t nEvents = Data()->GetNEvents();
963 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"Evaluation of " << GetMethodName() <<
" on "
966 mvaProb->
Resize( nEvents );
968 if (modulo <= 0 ) modulo = 1;
969 for (
Int_t ievt=0; ievt<nEvents; ievt++) {
971 Data()->SetCurrentEvent(ievt);
973 if (proba < 0)
break;
974 mvaProb->
SetValue( proba, ievt, DataInfo().IsSignal( Data()->GetEvent()) );
980 Log() << kDEBUG <<
Form(
"Dataset[%s] : ",DataInfo().GetName())
981 <<
"Elapsed time for evaluation of " << nEvents <<
" events: "
1000 Data()->SetCurrentType(
type);
1002 bias = 0; biasT = 0; dev = 0; devT = 0; rms = 0; rmsT = 0;
1004 Double_t m1 = 0, m2 = 0,
s1 = 0, s2 = 0, s12 = 0;
1005 const Int_t nevt = GetNEvents();
1010 Log() << kINFO <<
"Calculate regression for all events" <<
Endl;
1013 for (
Long64_t ievt=0; ievt<nevt; ievt++) {
1015 const Event* ev = Data()->GetEvent(ievt);
1018 Float_t r = GetRegressionValues()[0];
1037 m1 += t*
w;
s1 += t*t*
w;
1038 m2 +=
r*
w; s2 +=
r*
r*
w;
1041 if (ievt % modulo == 0)
1045 Log() << kINFO <<
"Elapsed time for evaluation of " << nevt <<
" events: "
1057 corr = s12/sumw - m1*m2;
1058 corr /=
TMath::Sqrt( (
s1/sumw - m1*m1) * (s2/sumw - m2*m2) );
1068 for (
Long64_t ievt=0; ievt<nevt; ievt++) {
1070 hist->
Fill( rV[ievt], tV[ievt], wV[ievt] );
1071 if (
d >= devMin &&
d <= devMax) {
1073 biasT += wV[ievt] *
d;
1075 rmsT += wV[ievt] *
d *
d;
1076 histT->
Fill( rV[ievt], tV[ievt], wV[ievt] );
1093 Data()->SetCurrentType(savedType);
1103 if (!resMulticlass) Log() << kFATAL<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"unable to create pointer in TestMulticlass, exiting."<<
Endl;
1112 TString histNamePrefix(GetTestvarName());
1113 TString histNamePrefixTest{histNamePrefix +
"_Test"};
1114 TString histNamePrefixTrain{histNamePrefix +
"_Train"};
1135 if (0==mvaRes && !(GetMethodTypeName().Contains(
"Cuts"))) {
1136 Log()<<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"mvaRes " << mvaRes <<
" GetMethodTypeName " << GetMethodTypeName()
1137 <<
" contains " << !(GetMethodTypeName().Contains(
"Cuts")) <<
Endl;
1138 Log() << kFATAL<<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"<TestInit> Test variable " << GetTestvarName()
1139 <<
" not found in tree" <<
Endl;
1144 fMeanS, fMeanB, fRmsS, fRmsB, fXmin, fXmax, fSignalClass );
1152 fCutOrientation = (fMeanS > fMeanB) ? kPositive : kNegative;
1162 if(IsSilentFile()) {
1163 TestvarName =
TString::Format(
"[%s]%s",DataInfo().GetName(),GetTestvarName().Data());
1165 TestvarName=GetTestvarName();
1167 TH1* mva_s =
new TH1D( TestvarName +
"_S",TestvarName +
"_S", fNbinsMVAoutput, fXmin, sxmax );
1168 TH1* mva_b =
new TH1D( TestvarName +
"_B",TestvarName +
"_B", fNbinsMVAoutput, fXmin, sxmax );
1169 mvaRes->
Store(mva_s,
"MVA_S");
1170 mvaRes->
Store(mva_b,
"MVA_B");
1180 proba_s =
new TH1D( TestvarName +
"_Proba_S", TestvarName +
"_Proba_S", fNbinsMVAoutput, 0.0, 1.0 );
1181 proba_b =
new TH1D( TestvarName +
"_Proba_B", TestvarName +
"_Proba_B", fNbinsMVAoutput, 0.0, 1.0 );
1182 mvaRes->
Store(proba_s,
"Prob_S");
1183 mvaRes->
Store(proba_b,
"Prob_B");
1188 rarity_s =
new TH1D( TestvarName +
"_Rarity_S", TestvarName +
"_Rarity_S", fNbinsMVAoutput, 0.0, 1.0 );
1189 rarity_b =
new TH1D( TestvarName +
"_Rarity_B", TestvarName +
"_Rarity_B", fNbinsMVAoutput, 0.0, 1.0 );
1190 mvaRes->
Store(rarity_s,
"Rar_S");
1191 mvaRes->
Store(rarity_b,
"Rar_B");
1197 TH1* mva_eff_s =
new TH1D( TestvarName +
"_S_high", TestvarName +
"_S_high", fNbinsH, fXmin, sxmax );
1198 TH1* mva_eff_b =
new TH1D( TestvarName +
"_B_high", TestvarName +
"_B_high", fNbinsH, fXmin, sxmax );
1199 mvaRes->
Store(mva_eff_s,
"MVA_HIGHBIN_S");
1200 mvaRes->
Store(mva_eff_b,
"MVA_HIGHBIN_B");
1209 Log() << kHEADER <<
Form(
"[%s] : ",DataInfo().GetName())<<
"Loop over test events and fill histograms with classifier response..." <<
Endl <<
Endl;
1210 if (mvaProb) Log() << kINFO <<
"Also filling probability and rarity histograms (on request)..." <<
Endl;
1214 if ( mvaRes->
GetSize() != GetNEvents() ) {
1215 Log() << kFATAL <<
TString::Format(
"Inconsistent result size %lld with number of events %u ", mvaRes->
GetSize() , GetNEvents() ) <<
Endl;
1216 assert(mvaRes->
GetSize() == GetNEvents());
1219 for (
Long64_t ievt=0; ievt<GetNEvents(); ievt++) {
1221 const Event* ev = GetEvent(ievt);
1225 if (DataInfo().IsSignal(ev)) {
1229 proba_s->
Fill( (*mvaProb)[ievt][0],
w );
1230 rarity_s->
Fill( GetRarity(
v ),
w );
1233 mva_eff_s ->
Fill(
v,
w );
1239 proba_b->
Fill( (*mvaProb)[ievt][0],
w );
1240 rarity_b->
Fill( GetRarity(
v ),
w );
1242 mva_eff_b ->
Fill(
v,
w );
1257 if (fSplS) {
delete fSplS; fSplS = 0; }
1258 if (fSplB) {
delete fSplB; fSplB = 0; }
1272 tf << prefix <<
"#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-" << std::endl << prefix << std::endl;
1273 tf << prefix <<
"Method : " << GetMethodTypeName() <<
"::" << GetMethodName() << std::endl;
1274 tf.setf(std::ios::left);
1275 tf << prefix <<
"TMVA Release : " << std::setw(10) << GetTrainingTMVAVersionString() <<
" ["
1276 << GetTrainingTMVAVersionCode() <<
"]" << std::endl;
1277 tf << prefix <<
"ROOT Release : " << std::setw(10) << GetTrainingROOTVersionString() <<
" ["
1278 << GetTrainingROOTVersionCode() <<
"]" << std::endl;
1279 tf << prefix <<
"Creator : " << userInfo->
fUser << std::endl;
1283 tf << prefix <<
"Training events: " << Data()->GetNTrainingEvents() << std::endl;
1287 tf << prefix <<
"Analysis type : " <<
"[" << ((GetAnalysisType()==
Types::kRegression) ?
"Regression" :
"Classification") <<
"]" << std::endl;
1288 tf << prefix << std::endl;
1293 tf << prefix << std::endl << prefix <<
"#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-" << std::endl << prefix << std::endl;
1294 WriteOptionsToStream( tf, prefix );
1295 tf << prefix << std::endl;
1298 tf << prefix << std::endl << prefix <<
"#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-" << std::endl << prefix << std::endl;
1299 WriteVarsToStream( tf, prefix );
1300 tf << prefix << std::endl;
1317 AddRegressionOutput(
type );
1319 AddMulticlassOutput(
type );
1321 AddClassifierOutput(
type );
1323 AddClassifierOutputProb(
type );
1333 if (!parent)
return;
1338 AddInfoItem( gi,
"TMVA Release", GetTrainingTMVAVersionString() +
" [" +
gTools().StringFromInt(GetTrainingTMVAVersionCode()) +
"]" );
1339 AddInfoItem( gi,
"ROOT Release", GetTrainingROOTVersionString() +
" [" +
gTools().StringFromInt(GetTrainingROOTVersionCode()) +
"]");
1340 AddInfoItem( gi,
"Creator", userInfo->
fUser);
1344 AddInfoItem( gi,
"Training events",
gTools().StringFromInt(Data()->GetNTrainingEvents()));
1345 AddInfoItem( gi,
"TrainingTime",
gTools().StringFromDouble(
const_cast<TMVA::MethodBase*
>(
this)->GetTrainTime()));
1350 AddInfoItem( gi,
"AnalysisType", analysisType );
1354 AddOptionsXMLTo( parent );
1357 AddVarsXMLTo( parent );
1360 if (fModelPersistence)
1361 AddSpectatorsXMLTo( parent );
1364 AddClassesXMLTo(parent);
1367 if (DoRegression()) AddTargetsXMLTo(parent);
1370 GetTransformationHandler(
false).AddXMLTo( parent );
1374 if (fMVAPdfS) fMVAPdfS->AddXMLTo(pdfs);
1375 if (fMVAPdfB) fMVAPdfB->AddXMLTo(pdfs);
1378 AddWeightsXMLTo( parent );
1394 ReadWeightsFromStream( rf );
1407 TString tfname( GetWeightFileName() );
1412 <<
"Creating xml weight file: "
1417 gTools().
AddAttr(rootnode,
"Method", GetMethodTypeName() +
"::" + GetMethodName());
1418 WriteStateToXML(rootnode);
1430 TString tfname(GetWeightFileName());
1433 <<
"Reading weight file: "
1439 Log() << kFATAL <<
"Error parsing XML file " << tfname <<
Endl;
1442 ReadStateFromXML(rootnode);
1447 fb.open(tfname.
Data(),std::ios::in);
1448 if (!fb.is_open()) {
1449 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<ReadStateFromFile> "
1450 <<
"Unable to open input weight file: " << tfname <<
Endl;
1452 std::istream fin(&fb);
1453 ReadStateFromStream(fin);
1456 if (!fTxtWeightsOnly) {
1459 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Reading root weight file: "
1462 ReadStateFromStream( *rfile );
1472 ReadStateFromXML(rootnode);
1486 fMethodName = fullMethodName(fullMethodName.
Index(
"::")+2,fullMethodName.
Length());
1489 Log().SetSource( GetName() );
1491 <<
"Read method \"" << GetMethodName() <<
"\" of type \"" << GetMethodTypeName() <<
"\"" <<
Endl;
1501 if (nodeName==
"GeneralInfo") {
1506 while (antypeNode) {
1509 if (
name ==
"TrainingTime")
1512 if (
name ==
"AnalysisType") {
1518 else Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Analysis type " << val <<
" is not known." <<
Endl;
1521 if (
name ==
"TMVA Release" ||
name ==
"TMVA") {
1525 Log() << kDEBUG <<
Form(
"[%s] : ",DataInfo().GetName()) <<
"MVA method was trained with TMVA Version: " << GetTrainingTMVAVersionString() <<
Endl;
1528 if (
name ==
"ROOT Release" ||
name ==
"ROOT") {
1533 <<
"MVA method was trained with ROOT Version: " << GetTrainingROOTVersionString() <<
Endl;
1538 else if (nodeName==
"Options") {
1539 ReadOptionsFromXML(ch);
1543 else if (nodeName==
"Variables") {
1544 ReadVariablesFromXML(ch);
1546 else if (nodeName==
"Spectators") {
1547 ReadSpectatorsFromXML(ch);
1549 else if (nodeName==
"Classes") {
1550 if (DataInfo().GetNClasses()==0) ReadClassesFromXML(ch);
1552 else if (nodeName==
"Targets") {
1553 if (DataInfo().GetNTargets()==0 && DoRegression()) ReadTargetsFromXML(ch);
1555 else if (nodeName==
"Transformations") {
1556 GetTransformationHandler().ReadFromXML(ch);
1558 else if (nodeName==
"MVAPdfs") {
1560 if (fMVAPdfS) {
delete fMVAPdfS; fMVAPdfS=0; }
1561 if (fMVAPdfB) {
delete fMVAPdfB; fMVAPdfB=0; }
1565 fMVAPdfS =
new PDF(pdfname);
1566 fMVAPdfS->ReadXML(pdfnode);
1569 fMVAPdfB =
new PDF(pdfname);
1570 fMVAPdfB->ReadXML(pdfnode);
1573 else if (nodeName==
"Weights") {
1574 ReadWeightsFromXML(ch);
1577 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Unparsed XML node: '" << nodeName <<
"'" <<
Endl;
1584 if (GetTransformationHandler().GetCallerName() ==
"") GetTransformationHandler().SetCallerName( GetName() );
1600 while (!
TString(buf).BeginsWith(
"Method")) GetLine(fin,buf);
1604 methodType = methodType(methodType.
Last(
' '),methodType.
Length());
1609 if (methodName ==
"") methodName = methodType;
1610 fMethodName = methodName;
1612 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Read method \"" << GetMethodName() <<
"\" of type \"" << GetMethodTypeName() <<
"\"" <<
Endl;
1615 Log().SetSource( GetName() );
1629 while (!
TString(buf).BeginsWith(
"#OPT")) GetLine(fin,buf);
1630 ReadOptionsFromStream(fin);
1634 fin.getline(buf,512);
1635 while (!
TString(buf).BeginsWith(
"#VAR")) fin.getline(buf,512);
1636 ReadVarsFromStream(fin);
1641 if (IsNormalised()) {
1647 if ( fVarTransformString ==
"None") {
1650 }
else if ( fVarTransformString ==
"Decorrelate" ) {
1652 }
else if ( fVarTransformString ==
"PCA" ) {
1653 varTrafo = GetTransformationHandler().AddTransformation(
new VariablePCATransform(DataInfo()), -1 );
1654 }
else if ( fVarTransformString ==
"Uniform" ) {
1655 varTrafo = GetTransformationHandler().AddTransformation(
new VariableGaussTransform(DataInfo(),
"Uniform"), -1 );
1656 }
else if ( fVarTransformString ==
"Gauss" ) {
1658 }
else if ( fVarTransformString ==
"GaussDecorr" ) {
1662 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<ProcessOptions> Variable transform '"
1663 << fVarTransformString <<
"' unknown." <<
Endl;
1666 if (GetTransformationHandler().GetTransformationList().GetSize() > 0) {
1667 fin.getline(buf,512);
1668 while (!
TString(buf).BeginsWith(
"#MAT")) fin.getline(buf,512);
1671 varTrafo->ReadTransformationFromStream(fin, trafo );
1682 fin.getline(buf,512);
1683 while (!
TString(buf).BeginsWith(
"#MVAPDFS")) fin.getline(buf,512);
1684 if (fMVAPdfS != 0) {
delete fMVAPdfS; fMVAPdfS = 0; }
1685 if (fMVAPdfB != 0) {
delete fMVAPdfB; fMVAPdfB = 0; }
1686 fMVAPdfS =
new PDF(
TString(GetName()) +
" MVA PDF Sig");
1687 fMVAPdfB =
new PDF(
TString(GetName()) +
" MVA PDF Bkg");
1688 fMVAPdfS->SetReadingVersion( GetTrainingTMVAVersionCode() );
1689 fMVAPdfB->SetReadingVersion( GetTrainingTMVAVersionCode() );
1696 fin.getline(buf,512);
1697 while (!
TString(buf).BeginsWith(
"#WGT")) fin.getline(buf,512);
1698 fin.getline(buf,512);
1699 ReadWeightsFromStream( fin );
1702 if (GetTransformationHandler().GetCallerName() ==
"") GetTransformationHandler().SetCallerName( GetName() );
1712 o << prefix <<
"NVar " << DataInfo().GetNVariables() << std::endl;
1713 std::vector<VariableInfo>::const_iterator varIt = DataInfo().GetVariableInfos().begin();
1714 for (; varIt!=DataInfo().GetVariableInfos().end(); ++varIt) { o << prefix; varIt->WriteToStream(o); }
1715 o << prefix <<
"NSpec " << DataInfo().GetNSpectators() << std::endl;
1716 varIt = DataInfo().GetSpectatorInfos().begin();
1717 for (; varIt!=DataInfo().GetSpectatorInfos().end(); ++varIt) { o << prefix; varIt->WriteToStream(o); }
1729 istr >> dummy >> readNVar;
1731 if (readNVar!=DataInfo().GetNVariables()) {
1732 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"You declared "<< DataInfo().GetNVariables() <<
" variables in the Reader"
1733 <<
" while there are " << readNVar <<
" variables declared in the file"
1739 std::vector<VariableInfo>::iterator varIt = DataInfo().GetVariableInfos().begin();
1741 for (; varIt!=DataInfo().GetVariableInfos().end(); ++varIt, ++varIdx) {
1748 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"ERROR in <ReadVarsFromStream>" <<
Endl;
1749 Log() << kINFO <<
"The definition (or the order) of the variables found in the input file is" <<
Endl;
1750 Log() << kINFO <<
"is not the same as the one declared in the Reader (which is necessary for" <<
Endl;
1751 Log() << kINFO <<
"the correct working of the method):" <<
Endl;
1752 Log() << kINFO <<
" var #" << varIdx <<
" declared in Reader: " << varIt->GetExpression() <<
Endl;
1753 Log() << kINFO <<
" var #" << varIdx <<
" declared in file : " << varInfo.
GetExpression() <<
Endl;
1754 Log() << kFATAL <<
"The expression declared to the Reader needs to be checked (name or order are wrong)" <<
Endl;
1767 for (
UInt_t idx=0; idx<DataInfo().GetVariableInfos().size(); idx++) {
1783 for (
UInt_t idx=0; idx<DataInfo().GetSpectatorInfos().size(); idx++) {
1785 VariableInfo& vi = DataInfo().GetSpectatorInfos()[idx];
1803 UInt_t nClasses=DataInfo().GetNClasses();
1808 for (
UInt_t iCls=0; iCls<nClasses; ++iCls) {
1809 ClassInfo *classInfo=DataInfo().GetClassInfo (iCls);
1826 for (
UInt_t idx=0; idx<DataInfo().GetTargetInfos().size(); idx++) {
1842 if (readNVar!=DataInfo().GetNVariables()) {
1843 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"You declared "<< DataInfo().GetNVariables() <<
" variables in the Reader"
1844 <<
" while there are " << readNVar <<
" variables declared in the file"
1854 existingVarInfo = DataInfo().GetVariableInfos()[varIdx];
1859 existingVarInfo = readVarInfo;
1862 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"ERROR in <ReadVariablesFromXML>" <<
Endl;
1863 Log() << kINFO <<
"The definition (or the order) of the variables found in the input file is" <<
Endl;
1864 Log() << kINFO <<
"not the same as the one declared in the Reader (which is necessary for the" <<
Endl;
1865 Log() << kINFO <<
"correct working of the method):" <<
Endl;
1866 Log() << kINFO <<
" var #" << varIdx <<
" declared in Reader: " << existingVarInfo.
GetExpression() <<
Endl;
1867 Log() << kINFO <<
" var #" << varIdx <<
" declared in file : " << readVarInfo.
GetExpression() <<
Endl;
1868 Log() << kFATAL <<
"The expression declared to the Reader needs to be checked (name or order are wrong)" <<
Endl;
1882 if (readNSpec!=DataInfo().GetNSpectators(
kFALSE)) {
1883 Log() << kFATAL<<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"You declared "<< DataInfo().GetNSpectators(
kFALSE) <<
" spectators in the Reader"
1884 <<
" while there are " << readNSpec <<
" spectators declared in the file"
1894 existingSpecInfo = DataInfo().GetSpectatorInfos()[specIdx];
1899 existingSpecInfo = readSpecInfo;
1902 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"ERROR in <ReadSpectatorsFromXML>" <<
Endl;
1903 Log() << kINFO <<
"The definition (or the order) of the spectators found in the input file is" <<
Endl;
1904 Log() << kINFO <<
"not the same as the one declared in the Reader (which is necessary for the" <<
Endl;
1905 Log() << kINFO <<
"correct working of the method):" <<
Endl;
1906 Log() << kINFO <<
" spec #" << specIdx <<
" declared in Reader: " << existingSpecInfo.
GetExpression() <<
Endl;
1907 Log() << kINFO <<
" spec #" << specIdx <<
" declared in file : " << readSpecInfo.
GetExpression() <<
Endl;
1908 Log() << kFATAL <<
"The expression declared to the Reader needs to be checked (name or order are wrong)" <<
Endl;
1927 for (
UInt_t icls = 0; icls<readNCls;++icls) {
1929 DataInfo().AddClass(classname);
1937 DataInfo().AddClass(className);
1944 if (DataInfo().GetClassInfo(
"Signal") != 0) {
1945 fSignalClass = DataInfo().GetClassInfo(
"Signal")->GetNumber();
1949 if (DataInfo().GetClassInfo(
"Background") != 0) {
1950 fBackgroundClass = DataInfo().GetClassInfo(
"Background")->GetNumber();
1970 DataInfo().AddTarget(expression,
"",
"",0,0);
1982 if (fBaseDir != 0)
return fBaseDir;
1983 Log()<<kDEBUG<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
" Base Directory for " << GetMethodName() <<
" not set yet --> check if already there.." <<
Endl;
1985 if (IsSilentFile()) {
1986 Log() << kFATAL <<
Form(
"Dataset[%s] : ", DataInfo().GetName())
1987 <<
"MethodBase::BaseDir() - No directory exists when running a Method without output file. Enable the "
1988 "output when creating the factory"
1994 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"MethodBase::BaseDir() - MethodBaseDir() return a NULL pointer!" <<
Endl;
1996 TString defaultDir = GetMethodName();
2000 Log()<<kDEBUG<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
" Base Directory for " << GetMethodTypeName() <<
" does not exist yet--> created it" <<
Endl;
2001 sdir = methodDir->
mkdir(defaultDir);
2004 if (fModelPersistence) {
2007 wfilePath.
Write(
"TrainingPath" );
2008 wfileName.
Write(
"WeightFileName" );
2012 Log()<<kDEBUG<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
" Base Directory for " << GetMethodTypeName() <<
" existed, return it.." <<
Endl;
2022 if (fMethodBaseDir != 0) {
2023 return fMethodBaseDir;
2026 const char *datasetName = DataInfo().
GetName();
2028 Log() << kDEBUG <<
Form(
"Dataset[%s] : ", datasetName) <<
" Base Directory for " << GetMethodTypeName()
2029 <<
" not set yet --> check if already there.." <<
Endl;
2032 if (!factoryBaseDir)
return nullptr;
2033 fMethodBaseDir = factoryBaseDir->
GetDirectory(datasetName);
2034 if (!fMethodBaseDir) {
2035 fMethodBaseDir = factoryBaseDir->
mkdir(datasetName,
TString::Format(
"Base directory for dataset %s", datasetName).Data());
2036 if (!fMethodBaseDir) {
2037 Log() << kFATAL <<
"Can not create dir " << datasetName;
2041 fMethodBaseDir = fMethodBaseDir->GetDirectory(methodTypeDir.
Data());
2043 if (!fMethodBaseDir) {
2045 TString methodTypeDirHelpStr =
TString::Format(
"Directory for all %s methods", GetMethodTypeName().Data());
2046 fMethodBaseDir = datasetDir->
mkdir(methodTypeDir.
Data(), methodTypeDirHelpStr);
2047 Log() << kDEBUG <<
Form(
"Dataset[%s] : ", datasetName) <<
" Base Directory for " << GetMethodName()
2048 <<
" does not exist yet--> created it" <<
Endl;
2051 Log() << kDEBUG <<
Form(
"Dataset[%s] : ", datasetName)
2052 <<
"Return from MethodBaseDir() after creating base directory " <<
Endl;
2053 return fMethodBaseDir;
2070 fWeightFile = theWeightFile;
2078 if (fWeightFile!=
"")
return fWeightFile;
2083 TString wFileDir(GetWeightFileDir());
2084 TString wFileName = GetJobName() +
"_" + GetMethodName() +
2086 if (wFileDir.
IsNull() )
return wFileName;
2088 return ( wFileDir + (wFileDir[wFileDir.
Length()-1]==
'/' ?
"" :
"/")
2100 if (0 != fMVAPdfS) {
2101 fMVAPdfS->GetOriginalHist()->Write();
2102 fMVAPdfS->GetSmoothedHist()->Write();
2103 fMVAPdfS->GetPDFHist()->Write();
2105 if (0 != fMVAPdfB) {
2106 fMVAPdfB->GetOriginalHist()->Write();
2107 fMVAPdfB->GetSmoothedHist()->Write();
2108 fMVAPdfB->GetPDFHist()->Write();
2114 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<WriteEvaluationHistosToFile> Unknown result: "
2115 << GetMethodName() << (treetype==
Types::kTraining?
"/kTraining":
"/kTesting")
2116 <<
"/kMaxAnalysisType" <<
Endl;
2121 GetTransformationHandler().PlotVariables (GetEventCollection(
Types::kTesting ), BaseDir() );
2123 Log() << kINFO <<
TString::Format(
"Dataset[%s] : ",DataInfo().GetName())
2124 <<
" variable plots are not produces ! The number of variables is " << DataInfo().GetNVariables()
2144 fin.getline(buf,512);
2146 if (
line.BeginsWith(
"TMVA Release")) {
2150 std::stringstream s(code.
Data());
2151 s >> fTMVATrainingVersion;
2152 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"MVA method was trained with TMVA Version: " << GetTrainingTMVAVersionString() <<
Endl;
2154 if (
line.BeginsWith(
"ROOT Release")) {
2158 std::stringstream s(code.
Data());
2159 s >> fROOTTrainingVersion;
2160 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"MVA method was trained with ROOT Version: " << GetTrainingROOTVersionString() <<
Endl;
2162 if (
line.BeginsWith(
"Analysis type")) {
2166 std::stringstream s(code.
Data());
2167 std::string analysisType;
2169 if (analysisType ==
"regression" || analysisType ==
"Regression") SetAnalysisType(
Types::kRegression );
2170 else if (analysisType ==
"classification" || analysisType ==
"Classification") SetAnalysisType(
Types::kClassification );
2171 else if (analysisType ==
"multiclass" || analysisType ==
"Multiclass") SetAnalysisType(
Types::kMulticlass );
2172 else Log() << kFATAL <<
"Analysis type " << analysisType <<
" from weight-file not known!" << std::endl;
2174 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Method was trained for "
2194 if (mvaRes==0 || mvaRes->
GetSize()==0) {
2195 Log() << kERROR<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<CreateMVAPdfs> No result of classifier testing available" <<
Endl;
2202 TH1* histMVAPdfS =
new TH1D( GetMethodTypeName() +
"_tr_S", GetMethodTypeName() +
"_tr_S",
2203 fMVAPdfS->GetHistNBins( mvaRes->
GetSize() ), minVal, maxVal );
2204 TH1* histMVAPdfB =
new TH1D( GetMethodTypeName() +
"_tr_B", GetMethodTypeName() +
"_tr_B",
2205 fMVAPdfB->GetHistNBins( mvaRes->
GetSize() ), minVal, maxVal );
2209 histMVAPdfS->
Sumw2();
2210 histMVAPdfB->
Sumw2();
2215 Double_t theWeight = Data()->GetEvent(ievt)->GetWeight();
2217 if (DataInfo().IsSignal(Data()->GetEvent(ievt))) histMVAPdfS->
Fill( theVal, theWeight );
2218 else histMVAPdfB->
Fill( theVal, theWeight );
2227 histMVAPdfS->
Write();
2228 histMVAPdfB->
Write();
2231 fMVAPdfS->BuildPDF ( histMVAPdfS );
2232 fMVAPdfB->BuildPDF ( histMVAPdfB );
2233 fMVAPdfS->ValidatePDF( histMVAPdfS );
2234 fMVAPdfB->ValidatePDF( histMVAPdfB );
2236 if (DataInfo().GetNClasses() == 2) {
2237 Log() << kINFO<<
Form(
"Dataset[%s] : ",DataInfo().GetName())
2238 <<
TString::Format(
"<CreateMVAPdfs> Separation from histogram (PDF): %1.3f (%1.3f)",
2239 GetSeparation( histMVAPdfS, histMVAPdfB ), GetSeparation( fMVAPdfS, fMVAPdfB ) )
2251 if (!fMVAPdfS || !fMVAPdfB) {
2252 Log() << kINFO<<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"<GetProba> MVA PDFs for Signal and Background don't exist yet, we'll create them on demand" <<
Endl;
2255 Double_t sigFraction = DataInfo().GetTrainingSumSignalWeights() / (DataInfo().GetTrainingSumSignalWeights() + DataInfo().GetTrainingSumBackgrWeights() );
2258 return GetProba(mvaVal,sigFraction);
2266 if (!fMVAPdfS || !fMVAPdfB) {
2267 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetProba> MVA PDFs for Signal and Background don't exist" <<
Endl;
2270 Double_t p_s = fMVAPdfS->GetVal( mvaVal );
2271 Double_t p_b = fMVAPdfB->GetVal( mvaVal );
2273 Double_t denom = p_s*ap_sig + p_b*(1 - ap_sig);
2275 return (denom > 0) ? (p_s*ap_sig) / denom : -1;
2288 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetRarity> Required MVA PDF for Signal or Background does not exist: "
2289 <<
"select option \"CreateMVAPdfs\"" <<
Endl;
2304 Data()->SetCurrentType(
type);
2314 else if (list->
GetSize() > 2) {
2315 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetEfficiency> Wrong number of arguments"
2316 <<
" in string: " << theString
2317 <<
" | required format, e.g., Efficiency:0.05, or empty string" <<
Endl;
2325 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetEfficiency> Binning mismatch between signal and background histos" <<
Endl;
2333 TH1 * effhist = results->
GetHist(
"MVA_HIGHBIN_S");
2340 if (results->
DoesExist(
"MVA_EFF_S")==0) {
2343 TH1* eff_s =
new TH1D( GetTestvarName() +
"_effS", GetTestvarName() +
" (signal)", fNbinsH,
xmin,
xmax );
2344 TH1* eff_b =
new TH1D( GetTestvarName() +
"_effB", GetTestvarName() +
" (background)", fNbinsH,
xmin,
xmax );
2345 results->
Store(eff_s,
"MVA_EFF_S");
2346 results->
Store(eff_b,
"MVA_EFF_B");
2349 Int_t sign = (fCutOrientation == kPositive) ? +1 : -1;
2353 for (
UInt_t ievt=0; ievt<Data()->GetNEvents(); ievt++) {
2356 Bool_t isSignal = DataInfo().IsSignal(GetEvent(ievt));
2357 Float_t theWeight = GetEvent(ievt)->GetWeight();
2358 Float_t theVal = (*mvaRes)[ievt];
2361 TH1* theHist = isSignal ? eff_s : eff_b;
2364 if (isSignal) nevtS+=theWeight;
2368 if (sign > 0 && maxbin > fNbinsH)
continue;
2369 if (sign < 0 && maxbin < 1 )
continue;
2370 if (sign > 0 && maxbin < 1 ) maxbin = 1;
2371 if (sign < 0 && maxbin > fNbinsH) maxbin = fNbinsH;
2376 for (
Int_t ibin=maxbin+1; ibin<=fNbinsH; ibin++) theHist->
AddBinContent( ibin , theWeight );
2378 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetEfficiency> Mismatch in sign" <<
Endl;
2389 TH1* eff_BvsS =
new TH1D( GetTestvarName() +
"_effBvsS", GetTestvarName() +
"", fNbins, 0, 1 );
2390 results->
Store(eff_BvsS,
"MVA_EFF_BvsS");
2395 TH1* rej_BvsS =
new TH1D( GetTestvarName() +
"_rejBvsS", GetTestvarName() +
"", fNbins, 0, 1 );
2396 results->
Store(rej_BvsS);
2398 rej_BvsS->
SetYTitle(
"Backgr rejection (1-eff)" );
2401 TH1* inveff_BvsS =
new TH1D( GetTestvarName() +
"_invBeffvsSeff",
2402 GetTestvarName(), fNbins, 0, 1 );
2403 results->
Store(inveff_BvsS);
2405 inveff_BvsS->
SetYTitle(
"Inverse backgr. eff (1/eff)" );
2411 fSplRefS =
new TSpline1(
"spline2_signal",
new TGraph( eff_s ) );
2412 fSplRefB =
new TSpline1(
"spline2_background",
new TGraph( eff_b ) );
2426 for (
Int_t bini=1; bini<=fNbins; bini++) {
2439 if (effB>std::numeric_limits<double>::epsilon())
2448 Double_t effS = 0., rejB, effS_ = 0., rejB_ = 0.;
2449 Int_t nbins_ = 5000;
2450 for (
Int_t bini=1; bini<=nbins_; bini++) {
2453 effS = (bini - 0.5)/
Float_t(nbins_);
2454 rejB = 1.0 - fSpleffBvsS->Eval( effS );
2457 if ((effS - rejB)*(effS_ - rejB_) < 0)
break;
2464 SetSignalReferenceCut( cut );
2469 if (0 == fSpleffBvsS) {
2475 Double_t effS = 0, effB = 0, effS_ = 0, effB_ = 0;
2476 Int_t nbins_ = 1000;
2482 for (
Int_t bini=1; bini<=nbins_; bini++) {
2485 effS = (bini - 0.5)/
Float_t(nbins_);
2486 effB = fSpleffBvsS->Eval( effS );
2487 integral += (1.0 - effB);
2501 for (
Int_t bini=1; bini<=nbins_; bini++) {
2504 effS = (bini - 0.5)/
Float_t(nbins_);
2505 effB = fSpleffBvsS->Eval( effS );
2508 if ((effB - effBref)*(effB_ - effBref) <= 0)
break;
2514 effS = 0.5*(effS + effS_);
2517 if (nevtS > 0) effSerr =
TMath::Sqrt( effS*(1.0 - effS)/nevtS );
2542 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetTrainingEfficiency> Wrong number of arguments"
2543 <<
" in string: " << theString
2544 <<
" | required format, e.g., Efficiency:0.05" <<
Endl;
2557 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetTrainingEfficiency> Binning mismatch between signal and background histos"
2565 TH1 * effhist = results->
GetHist(
"MVA_HIGHBIN_S");
2570 if (results->
DoesExist(
"MVA_TRAIN_S")==0) {
2576 TH1* mva_s_tr =
new TH1D( GetTestvarName() +
"_Train_S",GetTestvarName() +
"_Train_S", fNbinsMVAoutput, fXmin, sxmax );
2577 TH1* mva_b_tr =
new TH1D( GetTestvarName() +
"_Train_B",GetTestvarName() +
"_Train_B", fNbinsMVAoutput, fXmin, sxmax );
2578 results->
Store(mva_s_tr,
"MVA_TRAIN_S");
2579 results->
Store(mva_b_tr,
"MVA_TRAIN_B");
2584 TH1* mva_eff_tr_s =
new TH1D( GetTestvarName() +
"_trainingEffS", GetTestvarName() +
" (signal)",
2586 TH1* mva_eff_tr_b =
new TH1D( GetTestvarName() +
"_trainingEffB", GetTestvarName() +
" (background)",
2588 results->
Store(mva_eff_tr_s,
"MVA_TRAINEFF_S");
2589 results->
Store(mva_eff_tr_b,
"MVA_TRAINEFF_B");
2592 Int_t sign = (fCutOrientation == kPositive) ? +1 : -1;
2594 std::vector<Double_t> mvaValues = GetMvaValues(0,Data()->GetNEvents());
2595 assert( (
Long64_t) mvaValues.size() == Data()->GetNEvents());
2598 for (
Int_t ievt=0; ievt<Data()->GetNEvents(); ievt++) {
2600 Data()->SetCurrentEvent(ievt);
2601 const Event* ev = GetEvent();
2606 TH1* theEffHist = DataInfo().IsSignal(ev) ? mva_eff_tr_s : mva_eff_tr_b;
2607 TH1* theClsHist = DataInfo().IsSignal(ev) ? mva_s_tr : mva_b_tr;
2609 theClsHist->
Fill( theVal, theWeight );
2613 if (sign > 0 && maxbin > fNbinsH)
continue;
2614 if (sign < 0 && maxbin < 1 )
continue;
2615 if (sign > 0 && maxbin < 1 ) maxbin = 1;
2616 if (sign < 0 && maxbin > fNbinsH) maxbin = fNbinsH;
2618 if (sign > 0)
for (
Int_t ibin=1; ibin<=maxbin; ibin++) theEffHist->
AddBinContent( ibin , theWeight );
2619 else for (
Int_t ibin=maxbin+1; ibin<=fNbinsH; ibin++) theEffHist->
AddBinContent( ibin , theWeight );
2632 TH1* eff_bvss =
new TH1D( GetTestvarName() +
"_trainingEffBvsS", GetTestvarName() +
"", fNbins, 0, 1 );
2634 TH1* rej_bvss =
new TH1D( GetTestvarName() +
"_trainingRejBvsS", GetTestvarName() +
"", fNbins, 0, 1 );
2635 results->
Store(eff_bvss,
"EFF_BVSS_TR");
2636 results->
Store(rej_bvss,
"REJ_BVSS_TR");
2642 if (fSplTrainRefS)
delete fSplTrainRefS;
2643 if (fSplTrainRefB)
delete fSplTrainRefB;
2644 fSplTrainRefS =
new TSpline1(
"spline2_signal",
new TGraph( mva_eff_tr_s ) );
2645 fSplTrainRefB =
new TSpline1(
"spline2_background",
new TGraph( mva_eff_tr_b ) );
2658 fEffS = results->
GetHist(
"MVA_TRAINEFF_S");
2659 for (
Int_t bini=1; bini<=fNbins; bini++) {
2677 fSplTrainEffBvsS =
new TSpline1(
"effBvsS",
new TGraph( eff_bvss ) );
2681 if (0 == fSplTrainEffBvsS)
return 0.0;
2684 Double_t effS = 0., effB, effS_ = 0., effB_ = 0.;
2685 Int_t nbins_ = 1000;
2686 for (
Int_t bini=1; bini<=nbins_; bini++) {
2689 effS = (bini - 0.5)/
Float_t(nbins_);
2690 effB = fSplTrainEffBvsS->Eval( effS );
2693 if ((effB - effBref)*(effB_ - effBref) <= 0)
break;
2698 return 0.5*(effS + effS_);
2707 if (!resMulticlass) Log() << kFATAL<<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"unable to create pointer in GetMulticlassEfficiency, exiting."<<
Endl;
2719 if (!resMulticlass) Log() << kFATAL<<
"unable to create pointer in GetMulticlassTrainingEfficiency, exiting."<<
Endl;
2721 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Determine optimal multiclass cuts for training data..." <<
Endl;
2722 for (
UInt_t icls = 0; icls<DataInfo().GetNClasses(); ++icls) {
2753 Log() << kFATAL <<
"Cannot get confusion matrix for non-multiclass analysis." << std::endl;
2757 Data()->SetCurrentType(
type);
2761 if (resMulticlass ==
nullptr) {
2762 Log() << kFATAL <<
Form(
"Dataset[%s] : ", DataInfo().GetName())
2763 <<
"unable to create pointer in GetMulticlassEfficiency, exiting." <<
Endl;
2778 Double_t rms = sqrt( fRmsS*fRmsS + fRmsB*fRmsB );
2780 return (rms > 0) ?
TMath::Abs(fMeanS - fMeanB)/rms : 0;
2804 if ((!pdfS && pdfB) || (pdfS && !pdfB))
2805 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetSeparation> Mismatch in pdfs" <<
Endl;
2806 if (!pdfS) pdfS = fSplS;
2807 if (!pdfB) pdfB = fSplB;
2809 if (!fSplS || !fSplB) {
2810 Log()<<kDEBUG<<
Form(
"[%s] : ",DataInfo().GetName())<<
"could not calculate the separation, distributions"
2811 <<
" fSplS or fSplB are not yet filled" <<
Endl;
2826 if ((!histS && histB) || (histS && !histB))
2827 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetROCIntegral(TH1D*, TH1D*)> Mismatch in hists" <<
Endl;
2829 if (histS==0 || histB==0)
return 0.;
2842 for (
UInt_t i=0; i<nsteps; i++) {
2848 return integral*step;
2860 if ((!pdfS && pdfB) || (pdfS && !pdfB))
2861 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetSeparation> Mismatch in pdfs" <<
Endl;
2862 if (!pdfS) pdfS = fSplS;
2863 if (!pdfB) pdfB = fSplB;
2865 if (pdfS==0 || pdfB==0)
return 0.;
2874 for (
UInt_t i=0; i<nsteps; i++) {
2878 return integral*step;
2888 Double_t& max_significance_value )
const
2893 Double_t effS(0),effB(0),significance(0);
2894 TH1D *temp_histogram =
new TH1D(
"temp",
"temp", fNbinsH, fXmin, fXmax );
2896 if (SignalEvents <= 0 || BackgroundEvents <= 0) {
2897 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<GetMaximumSignificance> "
2898 <<
"Number of signal or background events is <= 0 ==> abort"
2902 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Using ratio SignalEvents/BackgroundEvents = "
2903 << SignalEvents/BackgroundEvents <<
Endl;
2908 if ( (eff_s==0) || (eff_b==0) ) {
2909 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Efficiency histograms empty !" <<
Endl;
2910 Log() << kWARNING <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"no maximum cut found, return 0" <<
Endl;
2914 for (
Int_t bin=1; bin<=fNbinsH; bin++) {
2919 significance = sqrt(SignalEvents)*( effS )/sqrt( effS + ( BackgroundEvents / SignalEvents) * effB );
2929 delete temp_histogram;
2931 Log() << kINFO <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"Optimal cut at : " << max_significance <<
Endl;
2932 Log() << kINFO<<
Form(
"Dataset[%s] : ",DataInfo().GetName()) <<
"Maximum significance: " << max_significance_value <<
Endl;
2934 return max_significance;
2948 Data()->SetCurrentType(treeType);
2950 Long64_t entries = Data()->GetNEvents();
2954 Log() << kFATAL <<
Form(
"Dataset[%s] : ",DataInfo().GetName())<<
"<CalculateEstimator> Wrong tree type: " << treeType <<
Endl;
2957 UInt_t varIndex = DataInfo().FindVarIndex( theVarName );
2971 for (
Int_t ievt = 0; ievt < entries; ievt++) {
2973 const Event* ev = GetEvent(ievt);
2978 if (DataInfo().IsSignal(ev)) {
2980 meanS += weight*theVar;
2981 rmsS += weight*theVar*theVar;
2985 meanB += weight*theVar;
2986 rmsB += weight*theVar*theVar;
2992 meanS = meanS/sumwS;
2993 meanB = meanB/sumwB;
2997 Data()->SetCurrentType(previousTreeType);
3007 if (theClassFileName ==
"")
3008 classFileName = GetWeightFileDir() +
"/" + GetJobName() +
"_" + GetMethodName() +
".class.C";
3010 classFileName = theClassFileName;
3014 TString tfname( classFileName );
3016 <<
"Creating standalone class: "
3019 std::ofstream fout( classFileName );
3021 Log() << kFATAL <<
"<MakeClass> Unable to open file: " << classFileName <<
Endl;
3026 fout <<
"// Class: " << className << std::endl;
3027 fout <<
"// Automatically generated by MethodBase::MakeClass" << std::endl <<
"//" << std::endl;
3031 fout <<
"/* configuration options =====================================================" << std::endl << std::endl;
3032 WriteStateToStream( fout );
3034 fout <<
"============================================================================ */" << std::endl;
3037 fout <<
"" << std::endl;
3038 fout <<
"#include <array>" << std::endl;
3039 fout <<
"#include <vector>" << std::endl;
3040 fout <<
"#include <cmath>" << std::endl;
3041 fout <<
"#include <string>" << std::endl;
3042 fout <<
"#include <iostream>" << std::endl;
3043 fout <<
"" << std::endl;
3046 this->MakeClassSpecificHeader( fout, className );
3048 fout <<
"#ifndef IClassifierReader__def" << std::endl;
3049 fout <<
"#define IClassifierReader__def" << std::endl;
3051 fout <<
"class IClassifierReader {" << std::endl;
3053 fout <<
" public:" << std::endl;
3055 fout <<
" // constructor" << std::endl;
3056 fout <<
" IClassifierReader() : fStatusIsClean( true ) {}" << std::endl;
3057 fout <<
" virtual ~IClassifierReader() {}" << std::endl;
3059 fout <<
" // return classifier response" << std::endl;
3061 fout <<
" virtual std::vector<double> GetMulticlassValues( const std::vector<double>& inputValues ) const = 0;" << std::endl;
3063 fout <<
" virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;" << std::endl;
3066 fout <<
" // returns classifier status" << std::endl;
3067 fout <<
" bool IsStatusClean() const { return fStatusIsClean; }" << std::endl;
3069 fout <<
" protected:" << std::endl;
3071 fout <<
" bool fStatusIsClean;" << std::endl;
3072 fout <<
"};" << std::endl;
3074 fout <<
"#endif" << std::endl;
3076 fout <<
"class " << className <<
" : public IClassifierReader {" << std::endl;
3078 fout <<
" public:" << std::endl;
3080 fout <<
" // constructor" << std::endl;
3081 fout <<
" " << className <<
"( std::vector<std::string>& theInputVars )" << std::endl;
3082 fout <<
" : IClassifierReader()," << std::endl;
3083 fout <<
" fClassName( \"" << className <<
"\" )," << std::endl;
3084 fout <<
" fNvars( " << GetNvar() <<
" )" << std::endl;
3085 fout <<
" {" << std::endl;
3086 fout <<
" // the training input variables" << std::endl;
3087 fout <<
" const char* inputVars[] = { ";
3088 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
3089 fout <<
"\"" << GetOriginalVarName(ivar) <<
"\"";
3090 if (ivar<GetNvar()-1) fout <<
", ";
3092 fout <<
" };" << std::endl;
3094 fout <<
" // sanity checks" << std::endl;
3095 fout <<
" if (theInputVars.size() <= 0) {" << std::endl;
3096 fout <<
" std::cout << \"Problem in class \\\"\" << fClassName << \"\\\": empty input vector\" << std::endl;" << std::endl;
3097 fout <<
" fStatusIsClean = false;" << std::endl;
3098 fout <<
" }" << std::endl;
3100 fout <<
" if (theInputVars.size() != fNvars) {" << std::endl;
3101 fout <<
" std::cout << \"Problem in class \\\"\" << fClassName << \"\\\": mismatch in number of input values: \"" << std::endl;
3102 fout <<
" << theInputVars.size() << \" != \" << fNvars << std::endl;" << std::endl;
3103 fout <<
" fStatusIsClean = false;" << std::endl;
3104 fout <<
" }" << std::endl;
3106 fout <<
" // validate input variables" << std::endl;
3107 fout <<
" for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {" << std::endl;
3108 fout <<
" if (theInputVars[ivar] != inputVars[ivar]) {" << std::endl;
3109 fout <<
" std::cout << \"Problem in class \\\"\" << fClassName << \"\\\": mismatch in input variable names\" << std::endl" << std::endl;
3110 fout <<
" << \" for variable [\" << ivar << \"]: \" << theInputVars[ivar].c_str() << \" != \" << inputVars[ivar] << std::endl;" << std::endl;
3111 fout <<
" fStatusIsClean = false;" << std::endl;
3112 fout <<
" }" << std::endl;
3113 fout <<
" }" << std::endl;
3115 fout <<
" // initialize min and max vectors (for normalisation)" << std::endl;
3116 for (
UInt_t ivar = 0; ivar < GetNvar(); ivar++) {
3117 fout <<
" fVmin[" << ivar <<
"] = " << std::setprecision(15) << GetXmin( ivar ) <<
";" << std::endl;
3118 fout <<
" fVmax[" << ivar <<
"] = " << std::setprecision(15) << GetXmax( ivar ) <<
";" << std::endl;
3121 fout <<
" // initialize input variable types" << std::endl;
3122 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
3123 fout <<
" fType[" << ivar <<
"] = \'" << DataInfo().GetVariableInfo(ivar).GetVarType() <<
"\';" << std::endl;
3126 fout <<
" // initialize constants" << std::endl;
3127 fout <<
" Initialize();" << std::endl;
3129 if (GetTransformationHandler().GetTransformationList().GetSize() != 0) {
3130 fout <<
" // initialize transformation" << std::endl;
3131 fout <<
" InitTransform();" << std::endl;
3133 fout <<
" }" << std::endl;
3135 fout <<
" // destructor" << std::endl;
3136 fout <<
" virtual ~" << className <<
"() {" << std::endl;
3137 fout <<
" Clear(); // method-specific" << std::endl;
3138 fout <<
" }" << std::endl;
3140 fout <<
" // the classifier response" << std::endl;
3141 fout <<
" // \"inputValues\" is a vector of input values in the same order as the" << std::endl;
3142 fout <<
" // variables given to the constructor" << std::endl;
3144 fout <<
" std::vector<double> GetMulticlassValues( const std::vector<double>& inputValues ) const override;" << std::endl;
3146 fout <<
" double GetMvaValue( const std::vector<double>& inputValues ) const override;" << std::endl;
3149 fout <<
" private:" << std::endl;
3151 fout <<
" // method-specific destructor" << std::endl;
3152 fout <<
" void Clear();" << std::endl;
3154 if (GetTransformationHandler().GetTransformationList().GetSize()!=0) {
3155 fout <<
" // input variable transformation" << std::endl;
3156 GetTransformationHandler().MakeFunction(fout, className,1);
3157 fout <<
" void InitTransform();" << std::endl;
3158 fout <<
" void Transform( std::vector<double> & iv, int sigOrBgd ) const;" << std::endl;
3161 fout <<
" // common member variables" << std::endl;
3162 fout <<
" const char* fClassName;" << std::endl;
3164 fout <<
" const size_t fNvars;" << std::endl;
3165 fout <<
" size_t GetNvar() const { return fNvars; }" << std::endl;
3166 fout <<
" char GetType( int ivar ) const { return fType[ivar]; }" << std::endl;
3168 fout <<
" // normalisation of input variables" << std::endl;
3169 fout <<
" double fVmin[" << GetNvar() <<
"];" << std::endl;
3170 fout <<
" double fVmax[" << GetNvar() <<
"];" << std::endl;
3171 fout <<
" double NormVariable( double x, double xmin, double xmax ) const {" << std::endl;
3172 fout <<
" // normalise to output range: [-1, 1]" << std::endl;
3173 fout <<
" return 2*(x - xmin)/(xmax - xmin) - 1.0;" << std::endl;
3174 fout <<
" }" << std::endl;
3176 fout <<
" // type of input variable: 'F' or 'I'" << std::endl;
3177 fout <<
" char fType[" << GetNvar() <<
"];" << std::endl;
3179 fout <<
" // initialize internal variables" << std::endl;
3180 fout <<
" void Initialize();" << std::endl;
3182 fout <<
" std::vector<double> GetMulticlassValues__( const std::vector<double>& inputValues ) const;" << std::endl;
3184 fout <<
" double GetMvaValue__( const std::vector<double>& inputValues ) const;" << std::endl;
3186 fout <<
"" << std::endl;
3187 fout <<
" // private members (method specific)" << std::endl;
3190 MakeClassSpecific( fout, className );
3193 fout <<
"inline std::vector<double> " << className <<
"::GetMulticlassValues( const std::vector<double>& inputValues ) const" << std::endl;
3195 fout <<
"inline double " << className <<
"::GetMvaValue( const std::vector<double>& inputValues ) const" << std::endl;
3197 fout <<
"{" << std::endl;
3198 fout <<
" // classifier response value" << std::endl;
3200 fout <<
" std::vector<double> retval;" << std::endl;
3202 fout <<
" double retval = 0;" << std::endl;
3205 fout <<
" // classifier response, sanity check first" << std::endl;
3206 fout <<
" if (!IsStatusClean()) {" << std::endl;
3207 fout <<
" std::cout << \"Problem in class \\\"\" << fClassName << \"\\\": cannot return classifier response\"" << std::endl;
3208 fout <<
" << \" because status is dirty\" << std::endl;" << std::endl;
3209 fout <<
" }" << std::endl;
3210 fout <<
" else {" << std::endl;
3211 if (IsNormalised()) {
3212 fout <<
" // normalise variables" << std::endl;
3213 fout <<
" std::vector<double> iV;" << std::endl;
3214 fout <<
" iV.reserve(inputValues.size());" << std::endl;
3215 fout <<
" int ivar = 0;" << std::endl;
3216 fout <<
" for (std::vector<double>::const_iterator varIt = inputValues.begin();" << std::endl;
3217 fout <<
" varIt != inputValues.end(); varIt++, ivar++) {" << std::endl;
3218 fout <<
" iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));" << std::endl;
3219 fout <<
" }" << std::endl;
3220 if (GetTransformationHandler().GetTransformationList().GetSize() != 0 && GetMethodType() !=
Types::kLikelihood &&
3222 fout <<
" Transform( iV, -1 );" << std::endl;
3226 fout <<
" retval = GetMulticlassValues__( iV );" << std::endl;
3228 fout <<
" retval = GetMvaValue__( iV );" << std::endl;
3231 if (GetTransformationHandler().GetTransformationList().GetSize() != 0 && GetMethodType() !=
Types::kLikelihood &&
3233 fout <<
" std::vector<double> iV(inputValues);" << std::endl;
3234 fout <<
" Transform( iV, -1 );" << std::endl;
3236 fout <<
" retval = GetMulticlassValues__( iV );" << std::endl;
3238 fout <<
" retval = GetMvaValue__( iV );" << std::endl;
3242 fout <<
" retval = GetMulticlassValues__( inputValues );" << std::endl;
3244 fout <<
" retval = GetMvaValue__( inputValues );" << std::endl;
3248 fout <<
" }" << std::endl;
3250 fout <<
" return retval;" << std::endl;
3251 fout <<
"}" << std::endl;
3254 if (GetTransformationHandler().GetTransformationList().GetSize()!=0)
3255 GetTransformationHandler().MakeFunction(fout, className,2);
3267 std::streambuf* cout_sbuf = std::cout.rdbuf();
3268 std::ofstream* o = 0;
3269 if (
gConfig().WriteOptionsReference()) {
3270 Log() << kINFO <<
"Print Help message for class " << GetName() <<
" into file: " << GetReferenceFile() <<
Endl;
3271 o =
new std::ofstream( GetReferenceFile(), std::ios::app );
3273 Log() << kFATAL <<
"<PrintHelpMessage> Unable to append to output file: " << GetReferenceFile() <<
Endl;
3275 std::cout.rdbuf( o->rdbuf() );
3280 Log() << kINFO <<
Endl;
3282 <<
"================================================================"
3286 <<
"H e l p f o r M V A m e t h o d [ " << GetName() <<
" ] :"
3291 Log() <<
"Help for MVA method [ " << GetName() <<
" ] :" <<
Endl;
3299 Log() <<
"<Suppress this message by specifying \"!H\" in the booking option>" <<
Endl;
3301 <<
"================================================================"
3308 Log() <<
"# End of Message___" <<
Endl;
3311 std::cout.rdbuf( cout_sbuf );
3326 retval = fSplRefS->Eval( theCut );
3328 else retval = fEffS->GetBinContent( fEffS->FindBin( theCut ) );
3337 if (theCut-fXmin < eps) retval = (GetCutOrientation() == kPositive) ? 1.0 : 0.0;
3338 else if (fXmax-theCut < eps) retval = (GetCutOrientation() == kPositive) ? 0.0 : 1.0;
3351 if (GetTransformationHandler().GetTransformationList().GetEntries() <= 0) {
3352 return (Data()->GetEventCollection(
type));
3359 if (fEventCollections.at(idx) == 0) {
3360 fEventCollections.at(idx) = &(Data()->GetEventCollection(
type));
3361 fEventCollections.at(idx) = GetTransformationHandler().CalcTransformations(*(fEventCollections.at(idx)),
kTRUE);
3363 return *(fEventCollections.at(idx));
3371 UInt_t a = GetTrainingTMVAVersionCode() & 0xff0000;
a>>=16;
3372 UInt_t b = GetTrainingTMVAVersionCode() & 0x00ff00;
b>>=8;
3373 UInt_t c = GetTrainingTMVAVersionCode() & 0x0000ff;
3383 UInt_t a = GetTrainingROOTVersionCode() & 0xff0000;
a>>=16;
3384 UInt_t b = GetTrainingROOTVersionCode() & 0x00ff00;
b>>=8;
3385 UInt_t c = GetTrainingROOTVersionCode() & 0x0000ff;
3396 if (mvaRes != NULL) {
3399 TH1D *mva_s_tr =
dynamic_cast<TH1D*
> (mvaRes->
GetHist(
"MVA_TRAIN_S"));
3400 TH1D *mva_b_tr =
dynamic_cast<TH1D*
> (mvaRes->
GetHist(
"MVA_TRAIN_B"));
3402 if ( !mva_s || !mva_b || !mva_s_tr || !mva_b_tr)
return -1;
3404 if (SorB ==
's' || SorB ==
'S')
const Bool_t Use_Splines_for_Eff_
const Int_t NBIN_HIST_HIGH
#define ROOT_VERSION_CODE
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void data
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t r
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t result
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h length
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void value
Option_t Option_t TPoint TPoint const char y2
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
Option_t Option_t TPoint TPoint const char y1
TMatrixT< Double_t > TMatrixD
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
R__EXTERN TSystem * gSystem
#define TMVA_VERSION_CODE
Class to manage histogram axis.
Int_t Write(const char *name=nullptr, Int_t option=0, Int_t bufsize=0) override
Write all objects in this collection.
virtual Int_t GetSize() const
Return the capacity of the collection, i.e.
This class stores the date and time with a precision of one second in an unsigned 32 bit word (950130...
const char * AsString() const
Return the date & time as a string (ctime() format).
TObject * Get(const char *namecycle) override
Return pointer to object identified by namecycle.
Describe directory structure in memory.
virtual TDirectory * GetDirectory(const char *namecycle, Bool_t printError=false, const char *funcname="GetDirectory")
Find a directory using apath.
virtual Bool_t cd()
Change current directory to "this" directory.
virtual TDirectory * mkdir(const char *name, const char *title="", Bool_t returnExistingDirectory=kFALSE)
Create a sub-directory "a" or a hierarchy of sub-directories "a/b/c/...".
A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
void Close(Option_t *option="") override
Close a file.
A TGraph is an object made of two arrays X and Y with npoints each.
1-D histogram with a double per channel (see TH1 documentation)
1-D histogram with a float per channel (see TH1 documentation)
TH1 is the base class of all histogram classes in ROOT.
virtual Double_t GetBinCenter(Int_t bin) const
Return bin center for 1D histogram.
virtual void AddBinContent(Int_t bin)
Increment bin content by 1.
virtual Double_t GetMean(Int_t axis=1) const
For axis = 1,2 or 3 returns the mean value of the histogram along X,Y or Z axis.
virtual void SetXTitle(const char *title)
static void AddDirectory(Bool_t add=kTRUE)
Sets the flag controlling the automatic add of histograms in memory.
virtual Double_t GetMaximum(Double_t maxval=FLT_MAX) const
Return maximum value smaller than maxval of bins in the range, unless the value has been overridden b...
virtual Int_t GetNbinsX() const
virtual Int_t Fill(Double_t x)
Increment bin with abscissa X by 1.
virtual void SetBinContent(Int_t bin, Double_t content)
Set bin content see convention for numbering bins in TH1::GetBin In case the bin number is greater th...
virtual Int_t GetMaximumBin() const
Return location of bin with maximum value in the range.
virtual Double_t GetBinContent(Int_t bin) const
Return content of bin number bin.
virtual Int_t GetQuantiles(Int_t nprobSum, Double_t *q, const Double_t *probSum=nullptr)
Compute Quantiles for this histogram Quantile x_q of a probability distribution Function F is defined...
virtual void SetYTitle(const char *title)
virtual void Scale(Double_t c1=1, Option_t *option="")
Multiply this histogram by a constant c1.
virtual Int_t FindBin(Double_t x, Double_t y=0, Double_t z=0)
Return Global bin number corresponding to x,y,z.
virtual Double_t KolmogorovTest(const TH1 *h2, Option_t *option="") const
Statistical test of compatibility in shape between this histogram and h2, using Kolmogorov test.
virtual void Sumw2(Bool_t flag=kTRUE)
Create structure to store sum of squares of weights.
static Bool_t AddDirectoryStatus()
Static function: cannot be inlined on Windows/NT.
2-D histogram with a float per channel (see TH1 documentation)
Int_t Fill(Double_t) override
Invalid Fill method.
TObject * At(Int_t idx) const override
Returns the object at position idx. Returns 0 if idx is out of range.
Class that contains all the information of a class.
TString fWeightFileExtension
Int_t fMaxNumOfAllowedVariables
VariablePlotting & GetVariablePlotting()
class TMVA::Config::VariablePlotting fVariablePlotting
MsgLogger * fLogger
! message logger
Class that contains all the data information.
Class that contains all the data information.
Float_t GetValue(UInt_t ivar) const
return value of i'th variable
Double_t GetWeight() const
return the event weight - depending on whether the flag IgnoreNegWeightsInTraining is or not.
static void SetIsTraining(Bool_t)
when this static function is called, it sets the flag whether events with negative event weight shoul...
Float_t GetTarget(UInt_t itgt) const
static void SetIgnoreNegWeightsInTraining(Bool_t)
when this static function is called, it sets the flag whether events with negative event weight shoul...
Interface for all concrete MVA method implementations.
void Init(std::vector< TString > &graphTitles)
This function gets some title and it creates a TGraph for every title.
IPythonInteractive()
standard constructor
~IPythonInteractive()
standard destructor
void ClearGraphs()
This function sets the point number to 0 for all graphs.
void AddPoint(Double_t x, Double_t y1, Double_t y2)
This function is used only in 2 TGraph case, and it will add new data points to graphs.
Virtual base Class for all MVA method.
TDirectory * MethodBaseDir() const
returns the ROOT directory where all instances of the corresponding MVA method are stored
virtual Double_t GetKSTrainingVsTest(Char_t SorB, TString opt="X")
MethodBase(const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
standard constructor
virtual Double_t GetMvaValue(Double_t *errLower=nullptr, Double_t *errUpper=nullptr)=0
virtual Double_t GetSeparation(TH1 *, TH1 *) const
compute "separation" defined as
void ReadClassesFromXML(void *clsnode)
read number of classes from XML
void SetWeightFileDir(TString fileDir)
set directory of weight file
void WriteStateToXML(void *parent) const
general method used in writing the header of the weight files where the used variables,...
void DeclareBaseOptions()
define the options (their key words) that can be set in the option string here the options valid for ...
virtual void TestRegression(Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample
virtual void DeclareCompatibilityOptions()
options that are used ONLY for the READER to ensure backward compatibility they are hence without any...
virtual Double_t GetSignificance() const
compute significance of mean difference
virtual Double_t GetProba(const Event *ev)
const char * GetName() const
virtual TMatrixD GetMulticlassConfusionMatrix(Double_t effB, Types::ETreeType type)
Construct a confusion matrix for a multiclass classifier.
void PrintHelpMessage() const
prints out method-specific help method
virtual void WriteEvaluationHistosToFile(Types::ETreeType treetype)
writes all MVA evaluation histograms to file
virtual void TestMulticlass()
test multiclass classification
const std::vector< TMVA::Event * > & GetEventCollection(Types::ETreeType type)
returns the event collection (i.e.
virtual std::vector< Double_t > GetDataMvaValues(DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
get all the MVA values for the events of the given Data type
void SetupMethod()
setup of methods
TDirectory * BaseDir() const
returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are sto...
virtual std::vector< Float_t > GetMulticlassEfficiency(std::vector< std::vector< Float_t > > &purity)
void AddInfoItem(void *gi, const TString &name, const TString &value) const
xml writing
virtual void AddClassifierOutputProb(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
virtual Double_t GetEfficiency(const TString &, Types::ETreeType, Double_t &err)
fill background efficiency (resp.
TString GetTrainingTMVAVersionString() const
calculates the TMVA version string from the training version code on the fly
void Statistics(Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as th...
Bool_t GetLine(std::istream &fin, char *buf)
reads one line from the input stream checks for certain keywords and interprets the line if keywords ...
void ProcessSetup()
process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overr...
virtual std::vector< Double_t > GetMvaValues(Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
get all the MVA values for the events of the current Data type
virtual Bool_t IsSignalLike()
uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for...
virtual ~MethodBase()
destructor
virtual Double_t GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
plot significance, , curve for given number of signal and background events; returns cut for maximum ...
virtual Double_t GetTrainingEfficiency(const TString &)
void SetWeightFileName(TString)
set the weight file name (depreciated)
virtual void MakeClass(const TString &classFileName=TString("")) const
create reader class for method (classification only at present)
TString GetWeightFileName() const
retrieve weight file name
virtual void TestClassification()
initialization
void AddOutput(Types::ETreeType type, Types::EAnalysisType analysisType)
virtual void WriteMonitoringHistosToFile() const
write special monitoring histograms to file dummy implementation here --------------—
virtual void AddRegressionOutput(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
void InitBase()
default initialization called by all constructors
virtual void GetRegressionDeviation(UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const
void ReadStateFromXMLString(const char *xmlstr)
for reading from memory
void CreateMVAPdfs()
Create PDFs of the MVA output variables.
TString GetTrainingROOTVersionString() const
calculates the ROOT version string from the training version code on the fly
virtual Double_t GetValueForRoot(Double_t)
returns efficiency as function of cut
void ReadStateFromFile()
Function to write options and weights to file.
void WriteVarsToStream(std::ostream &tf, const TString &prefix="") const
write the list of variables (name, min, max) for a given data transformation method to the stream
void ReadVarsFromStream(std::istream &istr)
Read the variables (name, min, max) for a given data transformation method from the stream.
void ReadSpectatorsFromXML(void *specnode)
read spectator info from XML
void SetTestvarName(const TString &v="")
void ReadVariablesFromXML(void *varnode)
read variable info from XML
virtual std::map< TString, Double_t > OptimizeTuningParameters(TString fomType="ROCIntegral", TString fitType="FitGA")
call the Optimizer with the set of parameters and ranges that are meant to be tuned.
virtual std::vector< Float_t > GetMulticlassTrainingEfficiency(std::vector< std::vector< Float_t > > &purity)
void WriteStateToStream(std::ostream &tf) const
general method used in writing the header of the weight files where the used variables,...
virtual Double_t GetRarity(Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
compute rarity:
virtual void SetTuneParameters(std::map< TString, Double_t > tuneParameters)
set the tuning parameters according to the argument This is just a dummy .
void ReadStateFromStream(std::istream &tf)
read the header from the weight files of the different MVA methods
void AddVarsXMLTo(void *parent) const
write variable info to XML
void AddTargetsXMLTo(void *parent) const
write target info to XML
void ReadTargetsFromXML(void *tarnode)
read target info from XML
void ProcessBaseOptions()
the option string is decoded, for available options see "DeclareOptions"
void ReadStateFromXML(void *parent)
void NoErrorCalc(Double_t *const err, Double_t *const errUpper)
void WriteStateToFile() const
write options and weights to file note that each one text file for the main configuration information...
void AddClassesXMLTo(void *parent) const
write class info to XML
virtual void AddClassifierOutput(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
void AddSpectatorsXMLTo(void *parent) const
write spectator info to XML
virtual Double_t GetROCIntegral(TH1D *histS, TH1D *histB) const
calculate the area (integral) under the ROC curve as a overall quality measure of the classification
virtual void AddMulticlassOutput(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
virtual void CheckSetup()
check may be overridden by derived class (sometimes, eg, fitters are used which can only be implement...
void SetSource(const std::string &source)
PDF wrapper for histograms; uses user-defined spline interpolation.
Double_t GetVal(Double_t x) const
returns value PDF(x)
Double_t GetIntegral(Double_t xmin, Double_t xmax)
computes PDF integral within given ranges
Class that is the base-class for a vector of result.
void Resize(Int_t entries)
std::vector< Float_t > * GetValueVector()
void SetValue(Float_t value, Int_t ievt, Bool_t type)
set MVA response
Class which takes the results of a multiclass classification.
TMatrixD GetConfusionMatrix(Double_t effB)
Returns a confusion matrix where each class is pitted against each other.
Float_t GetAchievablePur(UInt_t cls)
std::vector< Double_t > GetBestMultiClassCuts(UInt_t targetClass)
calculate the best working point (optimal cut values) for the multiclass classifier
void CreateMulticlassHistos(TString prefix, Int_t nbins, Int_t nbins_high)
this function fills the mva response histos for multiclass classification
Float_t GetAchievableEff(UInt_t cls)
void CreateMulticlassPerformanceHistos(TString prefix)
Create performance graphs for this classifier a multiclass setting.
Class that is the base-class for a vector of result.
Class that is the base-class for a vector of result.
Bool_t DoesExist(const TString &alias) const
Returns true if there is an object stored in the result for a given alias, false otherwise.
void Store(TObject *obj, const char *alias=nullptr)
TH1 * GetHist(const TString &alias) const
TList * GetStorage() const
Root finding using Brents algorithm (translated from CERNLIB function RZERO)
Double_t Root(Double_t refValue)
Root finding using Brents algorithm; taken from CERNLIB function RZERO.
Linear interpolation of TGraph.
Timing information for training and evaluation of MVA methods.
Double_t ElapsedSeconds(void)
computes elapsed tim in seconds
TString GetElapsedTime(Bool_t Scientific=kTRUE)
returns pretty string with elapsed time
void DrawProgressBar(Int_t, const TString &comment="")
draws progress bar in color or B&W caution:
Singleton class for Global types used by TMVA.
@ kSignal
Never change this number - it is elsewhere assumed to be zero !
Class for type info of MVA input variable.
void ReadFromXML(void *varnode)
read VariableInfo from stream
const TString & GetExpression() const
void ReadFromStream(std::istream &istr)
read VariableInfo from stream
void AddToXML(void *varnode)
write class to XML
void SetExternalLink(void *p)
void * GetExternalLink() const
A TMultiGraph is a collection of TGraph (or derived) objects.
const char * GetName() const override
Returns name of object.
Collectable string class.
virtual Int_t Write(const char *name=nullptr, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.
void ToLower()
Change string to lower-case.
Int_t Atoi() const
Return integer value of string.
Bool_t EndsWith(const char *pat, ECaseCompare cmp=kExact) const
Return true if string ends with the specified string.
TSubString Strip(EStripType s=kTrailing, char c=' ') const
Return a substring of self stripped at beginning and/or end.
const char * Data() const
TString & ReplaceAll(const TString &s1, const TString &s2)
Ssiz_t Last(char c) const
Find last occurrence of a character c.
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
Ssiz_t Index(const char *pat, Ssiz_t i=0, ECaseCompare cmp=kExact) const
virtual const char * GetBuildNode() const
Return the build node name.
virtual int mkdir(const char *name, Bool_t recursive=kFALSE)
Make a file system directory.
virtual const char * WorkingDirectory()
Return working directory.
virtual UserGroup_t * GetUserInfo(Int_t uid)
Returns all user info in the UserGroup_t structure.
void SaveDoc(XMLDocPointer_t xmldoc, const char *filename, Int_t layout=1)
store document content to file if layout<=0, no any spaces or newlines will be placed between xmlnode...
void FreeDoc(XMLDocPointer_t xmldoc)
frees allocated document data and deletes document itself
XMLNodePointer_t DocGetRootElement(XMLDocPointer_t xmldoc)
returns root node of document
XMLDocPointer_t NewDoc(const char *version="1.0")
creates new xml document with provided version
XMLDocPointer_t ParseFile(const char *filename, Int_t maxbuf=100000)
Parses content of file and tries to produce xml structures.
XMLDocPointer_t ParseString(const char *xmlstring)
parses content of string and tries to produce xml structures
void DocSetRootElement(XMLDocPointer_t xmldoc, XMLNodePointer_t xmlnode)
set main (root) node for document
void CreateVariableTransforms(const TString &trafoDefinition, TMVA::DataSetInfo &dataInfo, TMVA::TransformationHandler &transformationHandler, TMVA::MsgLogger &log)
MsgLogger & Endl(MsgLogger &ml)
Short_t Max(Short_t a, Short_t b)
Returns the largest of a and b.
Double_t Sqrt(Double_t x)
Returns the square root of x.
Short_t Min(Short_t a, Short_t b)
Returns the smallest of a and b.
Short_t Abs(Short_t d)
Returns the absolute value of parameter Short_t d.