-
Notifications
You must be signed in to change notification settings - Fork 0
/
TMVATrainer_AllVtx_BvsL_reduced.C
171 lines (140 loc) · 9.2 KB
/
TMVATrainer_AllVtx_BvsL_reduced.C
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
#include "TChain.h"
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TObjString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TMVAGui.C"
#if not defined(__CINT__) || defined(__MAKECINT__)
// needs to be included when makecint runs (ACLIC)
#include "TMVA/Factory.h"
#include "TMVA/Tools.h"
#endif
using namespace TMVA;
void TMVATrainer(){
// This loads the library
TMVA::Tools::Instance();
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName = "TMVATrainingResults_AllVtx_BvsL_reduced.root";
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory is
// the only TMVA object you have to interact with
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVATrainer_AllVtx", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory->AddVariable("TagVarCSV_vertexCategory","TagVarCSV_vertexCategory","units",'F');
factory->AddVariable("TagVarCSV_jetNTracks","TagVarCSV_jetNTracks","units",'F');
//factory->AddVariable("TagVarCSV_trackSip2dSig_0","TagVarCSV_trackSip2dSig_0","units",'F');
//factory->AddVariable("TagVarCSV_trackSip2dSig_1","TagVarCSV_trackSip2dSig_1","units",'F');
//factory->AddVariable("TagVarCSV_trackSip2dSig_2","TagVarCSV_trackSip2dSig_2","units",'F');
//factory->AddVariable("TagVarCSV_trackSip2dSig_3","TagVarCSV_trackSip2dSig_3","units",'F');
factory->AddVariable("TagVarCSV_trackSip3dSig_0","TagVarCSV_trackSip3dSig_0","units",'F');
factory->AddVariable("TagVarCSV_trackSip3dSig_1","TagVarCSV_trackSip3dSig_1","units",'F');
factory->AddVariable("TagVarCSV_trackSip3dSig_2","TagVarCSV_trackSip3dSig_2","units",'F');
factory->AddVariable("TagVarCSV_trackSip3dSig_3","TagVarCSV_trackSip3dSig_3","units",'F');
//factory->AddVariable("TagVarCSV_trackPtRel_0","TagVarCSV_trackPtRel_0","units",'F');
//factory->AddVariable("TagVarCSV_trackPtRel_1","TagVarCSV_trackPtRel_1","units",'F');
//factory->AddVariable("TagVarCSV_trackPtRel_2","TagVarCSV_trackPtRel_2","units",'F');
//factory->AddVariable("TagVarCSV_trackPtRel_3","TagVarCSV_trackPtRel_3","units",'F');
factory->AddVariable("TagVarCSV_trackSip2dSigAboveCharm","TagVarCSV_trackSip2dSigAboveCharm","units",'F');
//factory->AddVariable("TagVarCSV_trackSip3dSigAboveCharm","TagVarCSV_trackSip3dSigAboveCharm","units",'F');
//factory->AddVariable("TagVarCSV_trackSumJetEtRatio","TagVarCSV_trackSumJetEtRatio","units",'F');
//factory->AddVariable("TagVarCSV_trackSumJetDeltaR","TagVarCSV_trackSumJetDeltaR","units",'F');
factory->AddVariable("TagVarCSV_jetNTracksEtaRel","TagVarCSV_jetNTracksEtaRel","units",'F');
factory->AddVariable("TagVarCSV_trackEtaRel_0","TagVarCSV_trackEtaRel_0","units",'F');
factory->AddVariable("TagVarCSV_trackEtaRel_1","TagVarCSV_trackEtaRel_1","units",'F');
factory->AddVariable("TagVarCSV_trackEtaRel_2","TagVarCSV_trackEtaRel_2","units",'F');
factory->AddVariable("TagVarCSV_jetNSecondaryVertices","TagVarCSV_jetNSecondaryVertices","units",'F');
factory->AddVariable("TagVarCSV_vertexMass","TagVarCSV_vertexMass","units",'F');
factory->AddVariable("TagVarCSV_vertexNTracks","TagVarCSV_vertexNTracks","units",'F');
factory->AddVariable("TagVarCSV_vertexEnergyRatio","TagVarCSV_vertexEnergyRatio","units",'F');
factory->AddVariable("TagVarCSV_vertexJetDeltaR","TagVarCSV_vertexJetDeltaR","units",'F');
factory->AddVariable("TagVarCSV_flightDistance2dSig","TagVarCSV_flightDistance2dSig","units",'F');
//factory->AddVariable("TagVarCSV_flightDistance3dSig","TagVarCSV_flightDistance3dSig","units",'F');
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
factory->AddSpectator("Jet_pt","Jet_pt","units",'F');
factory->AddSpectator("Jet_eta","Jet_eta","units",'F');
factory->AddSpectator("Jet_phi","Jet_phi","units",'F');
factory->AddSpectator("Jet_mass","Jet_mass","units",'F');
factory->AddSpectator("Jet_flavour","Jet_flavour","units",'F');
factory->AddSpectator("Jet_nbHadrons","Jet_nbHadrons","units",'F');
factory->AddSpectator("Jet_JP","Jet_JP","units",'F');
factory->AddSpectator("Jet_JBP","Jet_JBP","units",'F');
factory->AddSpectator("Jet_CSV","Jet_CSV","units",'F');
factory->AddSpectator("Jet_CSVIVF","Jet_CSVIVF","units",'F');
factory->AddSpectator("TagVarCSV_trackSip2dValAboveCharm","TagVarCSV_trackSip2dValAboveCharm","units",'F');
factory->AddSpectator("TagVarCSV_trackSip3dValAboveCharm","TagVarCSV_trackSip3dValAboveCharm","units",'F');
factory->AddSpectator("TagVarCSV_flightDistance2dVal","TagVarCSV_flightDistance2dVal","units",'F');
factory->AddSpectator("TagVarCSV_flightDistance3dVal","TagVarCSV_flightDistance3dVal","units",'F');
// Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
TString fname = "QCD_Pt-120to170_TuneZ2star_8TeV_pythia6_JetTaggingVariables_training.root";
TFile *input = TFile::Open( fname );
std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
// --- Register the training and test trees
TTree *sigTree = (TTree*)input->Get("tagVars/ttree");
TTree *bkgTree = (TTree*)input->Get("tagVars/ttree");
// // global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
// factory->SetInputTrees( tree,signalCut,backgroundCut );
factory->AddSignalTree ( sigTree, signalWeight );
factory->AddBackgroundTree( bkgTree, backgroundWeight );
// Apply additional cuts on the signal and background samples (can be different)
TCut signalCut = "abs(Jet_flavour)==5 && TagVarCSV_vertexCategory>=0";
TCut backgroundCut = "abs(Jet_flavour)!=5 && abs(Jet_flavour)!=4 && TagVarCSV_vertexCategory>=0";
// Tell the factory how to use the training and testing events
factory->PrepareTrainingAndTestTree( signalCut, backgroundCut,
"nTrain_Background=70000:nTest_Background=200000:SplitMode=Random:!V" );
// Gradient Boost
factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D3_BvsL_reduced",
"!H:!V:NTrees=1000:MaxDepth=3:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );
factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D5_BvsL_reduced",
"!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20" );
// // Adaptive Boost
// factory->BookMethod( TMVA::Types::kBDT, "BDT",
// "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );
// // Bagging
// factory->BookMethod( TMVA::Types::kBDT, "BDTB",
// "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );
// // Decorrelation + Adaptive Boost
// factory->BookMethod( TMVA::Types::kBDT, "BDTD",
// "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}