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TMVA MultiProcessing #858

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b191f1b
TMVA MultiProcessing
Aug 15, 2017
6b66171
Fixed clang-format issues
mammadhajili Aug 16, 2017
691f6cd
Loading files fixes and clang format
mammadhajili Aug 18, 2017
d34f639
Fixing clang-format issues
mammadhajili Aug 23, 2017
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5 changes: 5 additions & 0 deletions tmva/tmva/inc/LinkDef4.h
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Expand Up @@ -43,4 +43,9 @@
#pragma link C++ function TMVA::CreateVariableTransform;
#pragma link C++ function TMVA::DataLoaderCopy;

#pragma link C++ function TMVA::DataLoaderCopy;
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@ashlaban ashlaban Aug 23, 2017

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Should we also export the DataLoaderMP copy here?


#pragma link C++ class std::map<TString, Double_t>+;


#endif
16 changes: 10 additions & 6 deletions tmva/tmva/inc/TMVA/Config.h
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@@ -1,4 +1,4 @@
// @(#)root/tmva $Id$
// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Joerg Stelzer, Fredrik Tegenfeldt, Helge Voss

/**********************************************************************************
Expand Down Expand Up @@ -47,7 +47,7 @@ namespace TMVA {
class MsgLogger;

class Config {

public:

static Config& Instance();
Expand All @@ -65,6 +65,9 @@ namespace TMVA {
Bool_t DrawProgressBar() const { return fDrawProgressBar; }
void SetDrawProgressBar( Bool_t d ) { fDrawProgressBar = d; }

UInt_t NWorkers() const { return fNWorkers; }
void SetNWorkers (UInt_t n) { fNWorkers = n; }

public:

class VariablePlotting;
Expand Down Expand Up @@ -97,8 +100,7 @@ namespace TMVA {
TString fWeightFileExtension;
TString fOptionsReferenceFileDir;
} fIONames; // Customisable weight file properties



private:

// private constructor
Expand All @@ -110,23 +112,25 @@ namespace TMVA {
static std::atomic<Config*> fgConfigPtr;
#else
static Config* fgConfigPtr;
#endif
#endif
private:

#if __cplusplus > 199711L
std::atomic<Bool_t> fUseColoredConsole; // coloured standard output
std::atomic<Bool_t> fSilent; // no output at all
std::atomic<Bool_t> fWriteOptionsReference; // if set true: Configurable objects write file with option reference
std::atomic<Bool_t> fDrawProgressBar; // draw progress bar to indicate training evolution
std::atomic<UInt_t> fNWorkers;
#else
Bool_t fUseColoredConsole; // coloured standard output
Bool_t fSilent; // no output at all
Bool_t fWriteOptionsReference; // if set true: Configurable objects write file with option reference
Bool_t fDrawProgressBar; // draw progress bar to indicate training evolution
UInt_t fNWorkers;
#endif
mutable MsgLogger* fLogger; // message logger
MsgLogger& Log() const { return *fLogger; }

ClassDef(Config,0); // Singleton class for global configuration settings
};

Expand Down
41 changes: 21 additions & 20 deletions tmva/tmva/inc/TMVA/DataLoader.h
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Expand Up @@ -33,12 +33,13 @@
#include <vector>
#include <map>
#include "TCut.h"
#include <memory>

#include "TMVA/Factory.h"
#include "TMVA/Types.h"
#include "TMVA/DataSet.h"
#include "TFile.h"

class TFile;
class TTree;
class TDirectory;
class TH2;
Expand Down Expand Up @@ -84,16 +85,15 @@ namespace TMVA {
// special case: signal/background

// Data input related
void SetInputTrees( const TString& signalFileName, const TString& backgroundFileName,
void SetInputTrees(const TString& signalFileName, const TString& backgroundFileName,
Double_t signalWeight=1.0, Double_t backgroundWeight=1.0 );
void SetInputTrees( TTree* inputTree, const TCut& SigCut, const TCut& BgCut );
void SetInputTrees( TTree *inputTree, const TCut& SigCut, const TCut& BgCut );
// Set input trees at once
void SetInputTrees( TTree* signal, TTree* background,
Double_t signalWeight=1.0, Double_t backgroundWeight=1.0) ;
void SetInputTrees( TTree *signal, TTree* background, Double_t signalWeight=1.0, Double_t backgroundWeight=1.0) ;

void AddSignalTree( TTree* signal, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddSignalTree( TTree *signal, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddSignalTree( TString datFileS, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddSignalTree( TTree* signal, Double_t weight, const TString& treetype );
void AddSignalTree( TTree *signal, Double_t weight, const TString& treetype );

// ... depreciated, kept for backwards compatibility
void SetSignalTree( TTree* signal, Double_t weight=1.0);
Expand All @@ -109,9 +109,9 @@ namespace TMVA {
void SetBackgroundWeightExpression( const TString& variable );

// special case: regression
void AddRegressionTree( TTree* tree, Double_t weight = 1.0,
Types::ETreeType treetype = Types::kMaxTreeType ) {
AddTree( tree, "Regression", weight, "", treetype );
void AddRegressionTree( TTree* tree, Double_t weight = 1.0,
Types::ETreeType treetype = Types::kMaxTreeType ) {
AddTree( tree, "Regression", weight, "", treetype );
}

// general
Expand Down Expand Up @@ -153,10 +153,10 @@ namespace TMVA {
void PrepareTrainingAndTestTree( const TCut& cut, const TString& splitOpt );
void PrepareTrainingAndTestTree( TCut sigcut, TCut bkgcut, const TString& splitOpt );

// ... deprecated, kept for backwards compatibility
// ... deprecated, kept for backwards compatibility
void PrepareTrainingAndTestTree( const TCut& cut, Int_t Ntrain, Int_t Ntest = -1 );

void PrepareTrainingAndTestTree( const TCut& cut, Int_t NsigTrain, Int_t NbkgTrain, Int_t NsigTest, Int_t NbkgTest,
void PrepareTrainingAndTestTree( const TCut& cut, Int_t NsigTrain, Int_t NbkgTrain, Int_t NsigTest, Int_t NbkgTest,
const TString& otherOpt="SplitMode=Random:!V" );

void PrepareTrainingAndTestTree( int foldNumber, Types::ETreeType tt );
Expand All @@ -168,15 +168,15 @@ namespace TMVA {
const DataSetInfo& GetDefaultDataSetInfo(){ return DefaultDataSetInfo(); }

TH2* GetCorrelationMatrix(const TString& className);

//Copy method use in VI and CV DEPRECATED: you can just call Clone DataLoader *dl2=(DataLoader *)dl1->Clone("dl2")
DataLoader* MakeCopy(TString name);
friend void DataLoaderCopy(TMVA::DataLoader* des, TMVA::DataLoader* src);
friend void DataLoaderCopy(TMVA::DataLoader* des, TMVA::DataLoader* src);
DataInputHandler& DataInput() { return *fDataInputHandler; }

private:


DataSetInfo& DefaultDataSetInfo();
void SetInputTreesFromEventAssignTrees();

Expand All @@ -188,7 +188,7 @@ namespace TMVA {

DataSetManager* fDataSetManager; // DSMTEST


DataInputHandler* fDataInputHandler;//->

std::vector<TMVA::VariableTransformBase*> fDefaultTrfs; // list of transformations on default DataSet
Expand All @@ -199,7 +199,7 @@ namespace TMVA {
Bool_t fVerbose; // verbose mode

// flag determining the way training and test data are assigned to DataLoader
enum DataAssignType { kUndefined = 0,
enum DataAssignType { kUndefined = 0,
kAssignTrees,
kAssignEvents };
DataAssignType fDataAssignType; // flags for data assigning
Expand All @@ -216,7 +216,7 @@ namespace TMVA {
Int_t fATreeType = 0; // type of event (=classIndex)
Float_t fATreeWeight = 0.0; // weight of the event
std::vector<Float_t> fATreeEvent; // event variables

Types::EAnalysisType fAnalysisType; // the training type

Bool_t fMakeFoldDataSet; // flag telling if the DataSet folds have been done
Expand All @@ -226,7 +226,8 @@ namespace TMVA {
ClassDef(DataLoader,3);
};
void DataLoaderCopy(TMVA::DataLoader* des, TMVA::DataLoader* src);
std::vector<std::shared_ptr<TFile>> DataLoaderCopyMP(TMVA::DataLoader *des, TMVA::DataLoader *src);
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@ashlaban ashlaban Aug 23, 2017

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Do these need to be public? Would it be better to use private with Envelope as a friend? I think the intention is that only the Envelope should be able to use it. I would then propose that we enforce this. We can always open the interface up later, but not the other way around.

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@etejedor etejedor Feb 20, 2018

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@mammadhajili could you answer the comments of @ashlaban please?

void DataLoaderCopyMPCloseFiles(std::vector<std::shared_ptr<TFile>> files);
} // namespace TMVA

#endif

10 changes: 5 additions & 5 deletions tmva/tmva/src/Config.cxx
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Expand Up @@ -53,11 +53,12 @@ TMVA::Config& TMVA::gConfig() { return TMVA::Config::Instance(); }
/// constructor - set defaults

TMVA::Config::Config() :
fUseColoredConsole ( kTRUE ),
fSilent ( kFALSE ),
fUseColoredConsole(kTRUE),
fSilent(kFALSE),
fWriteOptionsReference( kFALSE ),
fDrawProgressBar ( kFALSE ),
fLogger ( new MsgLogger("Config") )
fDrawProgressBar(kFALSE),
fNWorkers(1),
fLogger(new MsgLogger("Config"))
{
// plotting
fVariablePlotting.fTimesRMS = 8.0;
Expand Down Expand Up @@ -113,4 +114,3 @@ TMVA::Config& TMVA::Config::Instance()
return fgConfigPtr ? *fgConfigPtr :*(fgConfigPtr = new Config());
#endif
}

66 changes: 45 additions & 21 deletions tmva/tmva/src/CrossValidation.cxx
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Expand Up @@ -11,6 +11,7 @@
#include "TMVA/ResultsClassification.h"
#include "TMVA/tmvaglob.h"
#include "TMVA/Types.h"
#include "ROOT/TProcessExecutor.hxx"

#include "TSystem.h"
#include "TAxis.h"
Expand All @@ -20,6 +21,7 @@

#include <iostream>
#include <memory>
using namespace std;

/*! \class TMVA::CrossValidationResult
\ingroup TMVA
Expand Down Expand Up @@ -126,16 +128,17 @@ void TMVA::CrossValidation::Evaluate()
fFoldStatus=kTRUE;
}

// Process K folds
for(UInt_t i=0; i<fNumFolds; ++i){
Log() << kDEBUG << "Fold (" << methodTitle << "): " << i << Endl;
auto workItem = [&](UInt_t workerID) {

Log() << kDEBUG << "Fold (" << methodTitle << "): " << workerID << Endl;
// Get specific fold of dataset and setup method
TString foldTitle = methodTitle;
foldTitle += "_fold";
foldTitle += i+1;

fDataLoader->PrepareFoldDataSet(i, TMVA::Types::kTesting);
MethodBase* smethod = fClassifier->BookMethod(fDataLoader.get(), methodName, methodTitle, methodOptions);
foldTitle += workerID + 1;
auto classifier = std::unique_ptr<Factory>(new TMVA::Factory(
"CrossValidation","!V:!ROC:Silent:!ModelPersistence:!Color:!DrawProgressBar:AnalysisType=Classification"));
fDataLoader->PrepareFoldDataSet(workerID, TMVA::Types::kTesting);
MethodBase *smethod = classifier->BookMethod(fDataLoader.get(), methodName, methodTitle, methodOptions);

// Train method
Event::SetIsTraining(kTRUE);
Expand All @@ -147,10 +150,10 @@ void TMVA::CrossValidation::Evaluate()
smethod->TestClassification();

// Store results
fResults.fROCs[i] = fClassifier->GetROCIntegral(fDataLoader->GetName(),methodTitle);
auto res = classifier->GetROCIntegral(fDataLoader->GetName(), methodTitle);

TGraph* gr = fClassifier->GetROCCurve(fDataLoader->GetName(), methodTitle, true);
gr->SetLineColor(i+1);
TGraph* gr = classifier->GetROCCurve(fDataLoader->GetName(), methodTitle, true);
gr->SetLineColor(workerID + 1);
gr->SetLineWidth(2);
gr->SetTitle(foldTitle.Data());
fResults.fROCCurves->Add(gr);
Expand All @@ -159,24 +162,45 @@ void TMVA::CrossValidation::Evaluate()
fResults.fSeps.push_back(smethod->GetSeparation());

Double_t err;
fResults.fEff01s.push_back(smethod->GetEfficiency("Efficiency:0.01",Types::kTesting, err));
fResults.fEff10s.push_back(smethod->GetEfficiency("Efficiency:0.10",Types::kTesting,err));
fResults.fEff30s.push_back(smethod->GetEfficiency("Efficiency:0.30",Types::kTesting,err));
fResults.fEffAreas.push_back(smethod->GetEfficiency("" ,Types::kTesting,err));
fResults.fEff01s.push_back(smethod->GetEfficiency("Efficiency:0.01", Types::kTesting, err));
fResults.fEff10s.push_back(smethod->GetEfficiency("Efficiency:0.10", Types::kTesting, err));
fResults.fEff30s.push_back(smethod->GetEfficiency("Efficiency:0.30", Types::kTesting, err));
fResults.fEffAreas.push_back(smethod->GetEfficiency("" , Types::kTesting, err));
fResults.fTrainEff01s.push_back(smethod->GetTrainingEfficiency("Efficiency:0.01"));
fResults.fTrainEff10s.push_back(smethod->GetTrainingEfficiency("Efficiency:0.10"));
fResults.fTrainEff30s.push_back(smethod->GetTrainingEfficiency("Efficiency:0.30"));

// Clean-up for this fold
smethod->Data()->DeleteResults(smethod->GetMethodName(), Types::kTesting, Types::kClassification);
smethod->Data()->DeleteResults(smethod->GetMethodName(), Types::kTraining, Types::kClassification);
fClassifier->DeleteAllMethods();
fClassifier->fMethodsMap.clear();
}

TMVA::gConfig().SetSilent(kFALSE);
Log() << kINFO << "Evaluation done." << Endl;
TMVA::gConfig().SetSilent(kTRUE);
classifier->DeleteAllMethods();
classifier->fMethodsMap.clear();

return make_pair(res, workerID);
};
vector<pair<double, UInt_t>> res;

auto nWorkers = TMVA::gConfig().NWorkers();

if(nWorkers > 1) {
ROOT::TProcessExecutor workers(nWorkers);
res = workers.Map(workItem, ROOT::TSeqI(fNumFolds));
}

else {
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@ashlaban ashlaban Aug 23, 2017

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Use the structure

if () {
   ...
} else {
   ...
}

for(UInt_t i = 0; i < fNumFolds; ++ i) {
auto res_pair = workItem(i);
res.push_back(res_pair);
}
}

for(auto res_pair: res) {
fResults.fROCs[res_pair.second] = res_pair.first;
}

TMVA::gConfig().SetSilent(kFALSE);
Log() << kINFO << "Evaluation done." << Endl;
TMVA::gConfig().SetSilent(kTRUE);
}

const TMVA::CrossValidationResult& TMVA::CrossValidation::GetResults() const {
Expand Down
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