Train some classifiers

Runnning training can be done with the chanser_mva interface or via a Jupyter notebook (see tutorials/tmva) .

** Warning if your creation of the training data produced event with variables which are NaN or infinite then the training will fail. You can try and purge them at this point by filtering the trees, but better to put a check in the MVAParticleEventOut ** You can search for such variables in rOOT trees by doing things like

Here we use a chanser_mva and a ROOT script, RunTrainSignalID.C, which we can run with

  chanser_mva RunTrainSignalID.C 

The code include the following parts,

Get a tree of particle data that includes Final variables, I use the chanser::FiledTree class to simplify tree usage. Note the path to the data is given by the output directory giving in CreateTrainingData + $USERNAME/CANO_FILENAME/particleData/ParticleVariables_0.root

auto full = FiledTree::Read("particle",
"/work/dump/mvatraining/dglazier/Pi2_Pi2_Training__/particleData/ParticleVariables_0.root");

Turn off all Pi2 final variables apart from MissMass2 which we will cut on to define signal and background

full->Tree()->SetBranchStatus("Pi2*",0);
full->Tree()->SetBranchStatus("Pi2MissMass2",1);

Make a copy of the full data set filtered on signal-like events, in this case a cut on missing mass squared and DeltaTimeCuts, "TMath::Abs(Pi2MissMass2)<0.1&&DTCuts2==4" you may also filter events for NaNs and Infinity with code like "&&TMath::IsNaN(ElectronDeltaTime)==0&&TMath::Infinity()!=(PipDeltaTime)".

auto signal = FiledTree::RecreateCopyFull(full->Tree(),
     "/work/dump/mvatraining/dglazier/Pi2_Pi2_Training__/particleData/Signal.root",
     "TMath::Abs(Pi2MissMass2)<0.1&&DTCuts2==4");

And similar for background, now I select events outwith the exclusive peak (but still with some limit to try and descriminate better events that are closer to signal) and do not enforce particle cuts,

auto background = FiledTree::RecreateCopyFull(full->Tree(), "/work/dump/mvatraining/dglazier/Pi2_Pi2_Training__/particleData/Background.root", "TMath::Abs(Pi2MissMass2)<2&&TMath::Abs(Pi2MissMass2)>0.2");

// auto signal = FiledTree::Read("particle","/work/dump/mvatraining/dglazier/Pi2_Pi2_Training__/particleData/Signal.root"); //auto background = FiledTree::Read("particle","/work/dump/mvatraining/dglazier/Pi2_Pi2_Training__/particleData/Background.root");

auto train = TrainSignalID("mva2"); //The name string will be appended to the ouput directory, this (dir + name) must be specified when using MVASignalIDAction

//Give an output directory destination. Note the name in the cell above will also be added for this instance of the training. train.SetOutDir(""); //Set any branches in the tree that you which to ignore in the training train.IgnoreBranches("Pi2MissMass2:DTCuts2:EBCuts:Topo:NPerm:ElectronRegion"); //Provide the trees we got from the ROOT file above, note as they are FiledTrees I must use the .Tree() function. train.AddSignalTree(signal->Tree()); train.AddBackgroundTree(background->Tree()); //Set how many events you would like to use from each for testing and training train.SetNTrainTest(10000,10000); train.PrepareTrees();

//Standard TMVA Factory Method Booking train.BookMethod(TMVA::Types::kBDT, "BDT","!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20"); //Shortcut for predefined chanser_mva classifiers see MethConf.h // train.BookMethod(Meths.MLP);

train.DoTraining();

train.DrawROCCurve();

train.DrawResponses();