thailand2017/TMVATrainer.C File Reference

#include "TMVA/Factory.h"
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TRegexp.h"
#include "TEventList.h"
#include "TLeaf.h"
#include <iostream>
#include <map>
#include <vector>
#include <algorithm>
#include <utility>

Go to the source code of this file.

Macros
#define	TMVAgettype

Functions
int	gettype (TTree *t, TString varname)
int	SplitString (TString s, TString delim, TString *toks, int maxtoks)
TString	getFromCut (TString vars)
void	TMVATrainer (TString fname="", TString treename="", TString sigcut="", TString vars="", TString algo="BDT", TString precut="")

Macro Definition Documentation

#define TMVAgettype

Definition at line 16 of file thailand2017/TMVATrainer.C.

Function Documentation

TString getFromCut

(

TString

vars

)

Definition at line 50 of file thailand2017/TMVATrainer.C.

References i, n, res, SplitString(), TString, and v.

{
        TString toks[50];
        int n=SplitString(vars, "&&", toks, 50);
        TRegexp rvar("[_a-zA-Z][_a-zA-Z0-9]*");
        
        TString res=" ";
        
        for (int i=0;i<n;++i)
        {
                TString v = toks[i](rvar);
                if (v!="")
                {
                        if (res.Contains(" "+v+" ")) continue;
                        res+=v+" ";
                }
        }
        
        res = res.Strip(TString::kBoth);
        
        return res;
}

int gettype	(	TTree *	t,
		TString	varname
	)

Definition at line 18 of file thailand2017/TMVATrainer.C.

References TString.

{
        if (t->GetBranch(varname)==0) return -1;
        
        TString leaftype = t->GetLeaf(varname)->GetTypeName();
        
        if (leaftype=="Float_t")         return 0; 
        else if (leaftype=="Int_t")      return 1;  
        else if (leaftype=="Bool_t") return 2;
        
        return -1;
}

int SplitString	(	TString	s,
		TString	delim,
		TString *	toks,
		int	maxtoks
	)

Definition at line 33 of file thailand2017/TMVATrainer.C.

References i.

{
        TObjArray *tok = s.Tokenize(delim);
        int N = tok->GetEntries();      
        for (int i=0;i<N;++i) 
                if (i<maxtoks) 
                {
                        toks[i] = ((TObjString*)tok->At(i))->String();
                        toks[i].ReplaceAll("\t","");
                        toks[i] = toks[i].Strip(TString::kBoth);
                }
        return N;
}

void TMVATrainer	(	TString	fname = "",
		TString	treename = "",
		TString	sigcut = "",
		TString	vars = "",
		TString	algo = "BDT",
		TString	precut = ""
	)

Definition at line 76 of file thailand2017/TMVATrainer.C.

References f, getFromCut(), gettype(), i, nsig, SplitString(), t, TString, and vars.

{
        if ( fname=="" || treename=="" || sigcut=="" || vars=="" ) 
        {
                cout << "USAGE:\n";
                cout << "TMVATrainer.C( <input>, <tree>, <sigcut>, <vars>, [method], [precut] )\n\n";
                cout << "   <input>   : input file name containing TTree <tree>\n";
                cout << "   <tree>    : name of the TTree containing signal and background\n";
                cout << "   <sigcut>  : cut separating signal from background -> bgcut = !(sigcut)\n";
                cout << "   <vars>    : blank separated list with variables for training\n";
                cout << "   [method]  : optional method: 'BDT' (default), 'MLP' or 'LH'\n";
                cout << "   [precut]  : optional precut before training; has to be applied also before testing!\n\n";
                cout << "EXAMPLE:\n";
                cout << "root -l -b -q 'TMVATrainer.C(\"demodata.root\",\"ntp\",\"signal>0\",\"v1 v2 v3 v4 v5\",\"MLP\",\"\")'\n\n";
                return;
        }
        
        if (algo=="LH") algo="Likelihood";
        
        if (vars.Contains("&&")) vars = getFromCut(vars);
        cout <<"Vars : "<<vars<<endl;
        
        TString bkgcut="!("+sigcut+")";
        
        if (precut!="")
        {
                cout <<"Precut : "<<precut<<endl;
                sigcut += "&&" + precut;
                bkgcut += "&&" + precut;
        }
        
        TFile *f = TFile::Open(fname);
        TTree *t =(TTree*) f->Get(treename);
        
        TString outfname = fname;
        outfname.ReplaceAll(".root","");
        TString tmvaname = outfname+"_"+treename;
        outfname=outfname+"_tmva.root";
        
        TFile* outputFile = TFile::Open(outfname, "RECREATE" );
        
        TMVA::Factory *factory = new TMVA::Factory( tmvaname, outputFile, "Silent:!V:Transformations=I;N;D");

        TString toks[30];
        int N = SplitString(vars," ",toks,30);
        
        for (int i=0;i<N;++i)
        {
                int btype = gettype(t, toks[i]);
                if (btype==0 || btype==1)
                        factory->AddVariable(toks[i], btype==0?'F':'I');
        }   
        
        factory->AddTree( t, "Signal",     1.0, sigcut.Data());
        factory->AddTree( t, "Background", 1.0, bkgcut.Data());
        
        int nsig = t->GetEntries(sigcut);
        int nbkg = t->GetEntries(bkgcut);
        
        factory->PrepareTrainingAndTestTree( "", int(nsig*0.8), int(nbkg*0.8), int(nsig*0.19), int(nbkg*0.19)); 
        
        // old verion 4.1.3
        if (algo=="BDT") factory->BookMethod( TMVA::Types::kBDT, "BDT", "!V:nTrees=400:BoostType=AdaBoost:nCuts=10:NNodesMax=10" );
        // new verion 4.2.0
        // if (algo=="BDT") factory->BookMethod( TMVA::Types::kBDT, "BDT", "!V:nTrees=400:BoostType=AdaBoost:nCuts=10:MaxDepth=3:UseFisherCuts:DoPreselection" );
        else if (algo=="MLP") factory->BookMethod( TMVA::Types::kMLP, "MLP", "!V:NCycles=50:HiddenLayers=10,10:TestRate=5" );
        else if (algo=="Likelihood") factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood","!V:NAvEvtPerBin=50" );
        else {cout <<"Unconfigured algorithm! Exiting..."<<endl; return;}
        
        factory->TrainAllMethods();  
        //factory->TestAllMethods();
        //factory->EvaluateAllMethods();
        
        outputFile->Close();
        delete factory;
}