void TMVACrossValidation()
{
// This loads the library
TMVA::Tools::Instance();
// Load data
TFile *input(0);
TString fname = "./tmva_class_example.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
else {
TFile::SetCacheFileDir(".");
input = TFile::Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD");
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
TTree* signalTree = (TTree*)input->Get("TreeS");
TTree* background = (TTree*)input->Get("TreeB");
// Setup dataloader
TMVA::DataLoader* dataloader = new TMVA::DataLoader("dataset");
dataloader->AddSignalTree(signalTree);
dataloader->AddBackgroundTree(background);
dataloader->AddVariable("var1");
dataloader->AddVariable("var2");
dataloader->AddVariable("var3");
dataloader->AddVariable("var4");
dataloader->PrepareTrainingAndTestTree("", "SplitMode=Random:NormMode=NumEvents:!V");
// Setup cross-validation with Fisher method
TMVA::CrossValidation cv(dataloader);
cv.BookMethod(TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher");
// Run cross-validation and print results
cv.Evaluate();
TMVA::CrossValidationResult results = cv.GetResults();
results.Print();
}
void TMVACrossValidation()
{
// This loads the library
TMVA::Tools::Instance();
// Load data
TString fname = "./tmva_class_example.root";
if (gSystem->AccessPathName(fname))
gSystem->Exec("curl -O http://root.cern.ch/files/tmva_class_example.root");
TFile *input = TFile::Open(fname);
TTree* signalTree = (TTree*)input->Get("TreeS");
TTree* background = (TTree*)input->Get("TreeB");
// Setup dataloader
TMVA::DataLoader* dataloader = new TMVA::DataLoader("dataset");
dataloader->AddSignalTree(signalTree);
dataloader->AddBackgroundTree(background);
dataloader->AddVariable("var1");
dataloader->AddVariable("var2");
dataloader->AddVariable("var3");
dataloader->AddVariable("var4");
dataloader->PrepareTrainingAndTestTree("", "SplitMode=Random:NormMode=NumEvents:!V");
// Setup cross-validation with Fisher method
TMVA::CrossValidation cv(dataloader);
cv.BookMethod(TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher");
// Run cross-validation and print results
cv.Evaluate();
TMVA::CrossValidationResult results = cv.GetResults();
results.Print();
}
int testPyKerasRegression(){
// Get data file
std::cout << "Get test data..." << std::endl;
TString fname = "./tmva_reg_example.root";
if (gSystem->AccessPathName(fname)) // file does not exist in local directory
gSystem->Exec("curl -O http://root.cern.ch/files/tmva_reg_example.root");
TFile *input = TFile::Open(fname);
// Build model from python file
std::cout << "Generate keras model..." << std::endl;
UInt_t ret;
ret = gSystem->Exec("echo '"+pythonSrc+"' > generateKerasModelRegression.py");
if(ret!=0){
std::cout << "[ERROR] Failed to write python code to file" << std::endl;
return 1;
}
ret = gSystem->Exec("python generateKerasModelRegression.py");
if(ret!=0){
std::cout << "[ERROR] Failed to generate model using python" << std::endl;
return 1;
}
// Setup PyMVA and factory
std::cout << "Setup TMVA..." << std::endl;
TMVA::PyMethodBase::PyInitialize();
TFile* outputFile = TFile::Open("ResultsTestPyKerasRegression.root", "RECREATE");
TMVA::Factory *factory = new TMVA::Factory("testPyKerasRegression", outputFile,
"!V:Silent:Color:!DrawProgressBar:AnalysisType=Regression");
// Load data
TMVA::DataLoader *dataloader = new TMVA::DataLoader("datasetTestPyKerasRegression");
TTree *tree = (TTree*)input->Get("TreeR");
dataloader->AddRegressionTree(tree);
dataloader->AddVariable("var1");
dataloader->AddVariable("var2");
dataloader->AddTarget("fvalue");
dataloader->PrepareTrainingAndTestTree("",
"SplitMode=Random:NormMode=NumEvents:!V");
// Book and train method
factory->BookMethod(dataloader, TMVA::Types::kPyKeras, "PyKeras",
"!H:!V:VarTransform=D,G:FilenameModel=kerasModelRegression.h5:FilenameTrainedModel=trainedKerasModelRegression.h5:NumEpochs=10:BatchSize=32:SaveBestOnly=false:Verbose=0");
std::cout << "Train model..." << std::endl;
factory->TrainAllMethods();
// Clean-up
delete factory;
delete dataloader;
delete outputFile;
// Setup reader
UInt_t numEvents = 100;
std::cout << "Run reader and estimate target of " << numEvents << " events..." << std::endl;
TMVA::Reader *reader = new TMVA::Reader("!Color:Silent");
Float_t vars[3];
reader->AddVariable("var1", vars+0);
reader->AddVariable("var2", vars+1);
reader->BookMVA("PyKeras", "datasetTestPyKerasRegression/weights/testPyKerasRegression_PyKeras.weights.xml");
// Get mean squared error on events
tree->SetBranchAddress("var1", vars+0);
tree->SetBranchAddress("var2", vars+1);
tree->SetBranchAddress("fvalue", vars+2);
Float_t meanMvaError = 0;
for(UInt_t i=0; i<numEvents; i++){
tree->GetEntry(i);
meanMvaError += std::pow(vars[2]-reader->EvaluateMVA("PyKeras"),2);
}
meanMvaError = meanMvaError/float(numEvents);
// Check whether the response is obviously better than guessing
std::cout << "Mean squared error: " << meanMvaError << std::endl;
if(meanMvaError > 30.0){
std::cout << "[ERROR] Mean squared error is " << meanMvaError << " (>30.0)" << std::endl;
return 1;
}
return 0;
}
int testPyGTBMulticlass(){
// Get data file
std::cout << "Get test data..." << std::endl;
TString fname = "./tmva_example_multiple_background.root";
if (gSystem->AccessPathName(fname)){ // file does not exist in local directory
std::cout << "Create multiclass test data..." << std::endl;
TString createDataMacro = TString(gROOT->GetTutorialsDir()) + "/tmva/createData.C";
gROOT->ProcessLine(TString::Format(".L %s",createDataMacro.Data()));
gROOT->ProcessLine("create_MultipleBackground(200)");
std::cout << "Created " << fname << " for tests of the multiclass features" << std::endl;
}
TFile *input = TFile::Open(fname);
// Setup PyMVA and factory
std::cout << "Setup TMVA..." << std::endl;
TMVA::PyMethodBase::PyInitialize();
TFile* outputFile = TFile::Open("ResultsTestPyGTBMulticlass.root", "RECREATE");
TMVA::Factory *factory = new TMVA::Factory("testPyGTBMulticlass", outputFile,
"!V:Silent:Color:!DrawProgressBar:AnalysisType=multiclass");
// Load data
TMVA::DataLoader *dataloader = new TMVA::DataLoader("datasetTestPyGTBMulticlass");
TTree *signal = (TTree*)input->Get("TreeS");
TTree *background0 = (TTree*)input->Get("TreeB0");
TTree *background1 = (TTree*)input->Get("TreeB1");
TTree *background2 = (TTree*)input->Get("TreeB2");
dataloader->AddTree(signal, "Signal");
dataloader->AddTree(background0, "Background_0");
dataloader->AddTree(background1, "Background_1");
dataloader->AddTree(background2, "Background_2");
dataloader->AddVariable("var1");
dataloader->AddVariable("var2");
dataloader->AddVariable("var3");
dataloader->AddVariable("var4");
dataloader->PrepareTrainingAndTestTree("",
"SplitMode=Random:NormMode=NumEvents:!V");
// Book and train method
factory->BookMethod(dataloader, TMVA::Types::kPyGTB, "PyGTB",
"!H:!V:VarTransform=None:NEstimators=100:Verbose=0");
std::cout << "Train classifier..." << std::endl;
factory->TrainAllMethods();
// Clean-up
delete factory;
delete dataloader;
delete outputFile;
// Setup reader
UInt_t numEvents = 100;
std::cout << "Run reader and classify " << numEvents << " events..." << std::endl;
TMVA::Reader *reader = new TMVA::Reader("!Color:Silent");
Float_t vars[4];
reader->AddVariable("var1", vars+0);
reader->AddVariable("var2", vars+1);
reader->AddVariable("var3", vars+2);
reader->AddVariable("var4", vars+3);
reader->BookMVA("PyGTB", "datasetTestPyGTBMulticlass/weights/testPyGTBMulticlass_PyGTB.weights.xml");
// Get mean response of method on signal and background events
signal->SetBranchAddress("var1", vars+0);
signal->SetBranchAddress("var2", vars+1);
signal->SetBranchAddress("var3", vars+2);
signal->SetBranchAddress("var4", vars+3);
background0->SetBranchAddress("var1", vars+0);
background0->SetBranchAddress("var2", vars+1);
background0->SetBranchAddress("var3", vars+2);
background0->SetBranchAddress("var4", vars+3);
background1->SetBranchAddress("var1", vars+0);
background1->SetBranchAddress("var2", vars+1);
background1->SetBranchAddress("var3", vars+2);
background1->SetBranchAddress("var4", vars+3);
background2->SetBranchAddress("var1", vars+0);
background2->SetBranchAddress("var2", vars+1);
background2->SetBranchAddress("var3", vars+2);
background2->SetBranchAddress("var4", vars+3);
Float_t meanMvaSignal = 0;
Float_t meanMvaBackground0 = 0;
Float_t meanMvaBackground1 = 0;
Float_t meanMvaBackground2 = 0;
for(UInt_t i=0; i<numEvents; i++){
signal->GetEntry(i);
meanMvaSignal += reader->EvaluateMulticlass("PyGTB")[0];
background0->GetEntry(i);
meanMvaBackground0 += reader->EvaluateMulticlass("PyGTB")[1];
background1->GetEntry(i);
meanMvaBackground1 += reader->EvaluateMulticlass("PyGTB")[2];
background2->GetEntry(i);
meanMvaBackground2 += reader->EvaluateMulticlass("PyGTB")[3];
}
meanMvaSignal = meanMvaSignal/float(numEvents);
meanMvaBackground0 = meanMvaBackground0/float(numEvents);
meanMvaBackground1 = meanMvaBackground1/float(numEvents);
meanMvaBackground2 = meanMvaBackground2/float(numEvents);
// Check whether the response is obviously better than guessing
std::cout << "Mean MVA response on signal: " << meanMvaSignal << std::endl;
if(meanMvaSignal < 0.3){
std::cout << "[ERROR] Mean response on signal is " << meanMvaSignal << " (<0.3)" << std::endl;
return 1;
}
std::cout << "Mean MVA response on background 0: " << meanMvaBackground0 << std::endl;
if(meanMvaBackground0 < 0.3){
std::cout << "[ERROR] Mean response on background 0 is " << meanMvaBackground0 << " (<0.3)" << std::endl;
return 1;
}
std::cout << "Mean MVA response on background 1: " << meanMvaBackground1 << std::endl;
if(meanMvaBackground0 < 0.3){
std::cout << "[ERROR] Mean response on background 1 is " << meanMvaBackground1 << " (<0.3)" << std::endl;
return 1;
}
std::cout << "Mean MVA response on background 2: " << meanMvaBackground2 << std::endl;
if(meanMvaBackground0 < 0.3){
std::cout << "[ERROR] Mean response on background 2 is " << meanMvaBackground2 << " (<0.3)" << std::endl;
return 1;
}
return 0;
}