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Reference Guide
Classification.h
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1// @(#)root/tmva $Id$ 2017
2// Authors: Omar Zapata, Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss, Kai Voss, Eckhard von Toerne,
3// Jan Therhaag
4
5#ifndef ROOT_TMVA_Classification
6#define ROOT_TMVA_Classification
7
8#include <TString.h>
9#include <TMultiGraph.h>
10
11#include <TMVA/IMethod.h>
12#include <TMVA/MethodBase.h>
13#include <TMVA/Configurable.h>
14#include <TMVA/Types.h>
15#include <TMVA/DataSet.h>
16#include <TMVA/Event.h>
17#include <TMVA/Results.h>
20#include <TMVA/Factory.h>
21#include <TMVA/DataLoader.h>
22#include <TMVA/OptionMap.h>
23#include <TMVA/Envelope.h>
24
25/*! \class TMVA::ClassificationResult
26 * Class to save the results of the classifier.
27 * Every machine learning method booked have an object for the results
28 * in the classification process, in this class is stored the mvas,
29 * data loader name and ml method name and title.
30 * You can to display the resutls calling the method Show, get the ROC-integral with the
31 * method GetROCIntegral or get the TMVA::ROCCurve object calling GetROC.
32\ingroup TMVA
33*/
34
35/*! \class TMVA::Classification
36 * Class to perform two class classification.
37 * The first step before any analysis is to preperate the data,
38 * to do that you need to create an object of TMVA::DataLoader,
39 * in this object you need to configure the variables and the number of events
40 * to train/test.
41 * The class TMVA::Experimental::Classification needs a TMVA::DataLoader object,
42 * optional a TFile object to save the results and some extra options in a string
43 * like "V:Color:Transformations=I;D;P;U;G:Silent:DrawProgressBar:ModelPersistence:Jobs=2" where:
44 * V = verbose output
45 * Color = coloured screen output
46 * Silent = batch mode: boolean silent flag inhibiting any output from TMVA
47 * Transformations = list of transformations to test.
48 * DrawProgressBar = draw progress bar to display training and testing.
49 * ModelPersistence = to save the trained model in xml or serialized files.
50 * Jobs = number of ml methods to test/train in parallel using MultiProc, requires to call Evaluate method.
51 * Basic example.
52 * \code
53void classification(UInt_t jobs = 2)
54{
55 TMVA::Tools::Instance();
56
57 TFile *input(0);
58 TString fname = "./tmva_class_example.root";
59 if (!gSystem->AccessPathName(fname)) {
60 input = TFile::Open(fname); // check if file in local directory exists
61 } else {
62 TFile::SetCacheFileDir(".");
63 input = TFile::Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD");
64 }
65 if (!input) {
66 std::cout << "ERROR: could not open data file" << std::endl;
67 exit(1);
68 }
69
70 // Register the training and test trees
71
72 TTree *signalTree = (TTree *)input->Get("TreeS");
73 TTree *background = (TTree *)input->Get("TreeB");
74
75 TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
76
77 dataloader->AddVariable("myvar1 := var1+var2", 'F');
78 dataloader->AddVariable("myvar2 := var1-var2", "Expression 2", "", 'F');
79 dataloader->AddVariable("var3", "Variable 3", "units", 'F');
80 dataloader->AddVariable("var4", "Variable 4", "units", 'F');
81
82 dataloader->AddSpectator("spec1 := var1*2", "Spectator 1", "units", 'F');
83 dataloader->AddSpectator("spec2 := var1*3", "Spectator 2", "units", 'F');
84
85 // global event weights per tree (see below for setting event-wise weights)
86 Double_t signalWeight = 1.0;
87 Double_t backgroundWeight = 1.0;
88
89 dataloader->SetBackgroundWeightExpression("weight");
90
91 TMVA::Experimental::Classification *cl = new TMVA::Experimental::Classification(dataloader, Form("Jobs=%d", jobs));
92
93 cl->BookMethod(TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=2000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:"
94 "UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2");
95 cl->BookMethod(TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm");
96
97 cl->Evaluate(); // Train and Test all methods
98
99 auto &results = cl->GetResults();
100
101 TCanvas *c = new TCanvas(Form("ROC"));
102 c->SetTitle("ROC-Integral Curve");
103
104 auto mg = new TMultiGraph();
105 for (UInt_t i = 0; i < results.size(); i++) {
106 auto roc = results[i].GetROCGraph();
107 roc->SetLineColorAlpha(i + 1, 0.1);
108 mg->Add(roc);
109 }
110 mg->Draw("AL");
111 mg->GetXaxis()->SetTitle(" Signal Efficiency ");
112 mg->GetYaxis()->SetTitle(" Background Rejection ");
113 c->BuildLegend(0.15, 0.15, 0.3, 0.3);
114 c->Draw();
115
116 delete cl;
117}
118 * \endcode
119 *
120\ingroup TMVA
121*/
122
123namespace TMVA {
124class ResultsClassification;
125namespace Experimental {
127 friend class Classification;
128
129private:
132 std::map<UInt_t, std::vector<std::tuple<Float_t, Float_t, Bool_t>>> fMvaTrain; // Mvas for two-class classification
133 std::map<UInt_t, std::vector<std::tuple<Float_t, Float_t, Bool_t>>>
134 fMvaTest; // Mvas for two-class and multiclass classification
135 std::vector<TString> fClassNames; //
136
137 Bool_t IsMethod(TString methodname, TString methodtitle);
138 Bool_t fIsCuts; // if it is a method cuts need special output
140
141public:
145
146 const TString GetMethodName() const { return fMethod.GetValue<TString>("MethodName"); }
147 const TString GetMethodTitle() const { return fMethod.GetValue<TString>("MethodTitle"); }
152
153 void Show();
154
157
159};
160
161class Classification : public Envelope {
162 std::vector<ClassificationResult> fResults; //!
163 std::vector<IMethod *> fIMethods; //! vector of objects with booked methods
167public:
168 explicit Classification(DataLoader *loader, TFile *file, TString options);
169 explicit Classification(DataLoader *loader, TString options);
171
172 virtual void Train();
173 virtual void TrainMethod(TString methodname, TString methodtitle);
174 virtual void TrainMethod(Types::EMVA method, TString methodtitle);
175
176 virtual void Test();
177 virtual void TestMethod(TString methodname, TString methodtitle);
178 virtual void TestMethod(Types::EMVA method, TString methodtitle);
179
180 virtual void Evaluate();
181
182 std::vector<ClassificationResult> &GetResults();
183
184 MethodBase *GetMethod(TString methodname, TString methodtitle);
185
186protected:
187 TString GetMethodOptions(TString methodname, TString methodtitle);
188 Bool_t HasMethodObject(TString methodname, TString methodtitle, Int_t &index);
192 TMVA::ROCCurve *GetROC(TString methodname, TString methodtitle, UInt_t iClass = 0,
194
195 Double_t GetROCIntegral(TString methodname, TString methodtitle, UInt_t iClass = 0);
196
197 ClassificationResult &GetResults(TString methodname, TString methodtitle);
198 void CopyFrom(TDirectory *src, TFile *file);
199 void MergeFiles();
200
202};
203} // namespace Experimental
204} // namespace TMVA
205
206#endif // ROOT_TMVA_Classification
ROOT::R::TRInterface & r
Definition: Object.C:4
int Int_t
Definition: RtypesCore.h:41
unsigned int UInt_t
Definition: RtypesCore.h:42
bool Bool_t
Definition: RtypesCore.h:59
double Double_t
Definition: RtypesCore.h:55
#define ClassDef(name, id)
Definition: Rtypes.h:326
int type
Definition: TGX11.cxx:120
Describe directory structure in memory.
Definition: TDirectory.h:34
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:48
A Graph is a graphics object made of two arrays X and Y with npoints each.
Definition: TGraph.h:41
Abstract base class for all high level ml algorithms, you can book ml methods like BDT,...
Definition: Envelope.h:47
Double_t GetROCIntegral(UInt_t iClass=0, TMVA::Types::ETreeType type=TMVA::Types::kTesting)
Method to get ROC-Integral value from mvas.
TGraph * GetROCGraph(UInt_t iClass=0, TMVA::Types::ETreeType type=TMVA::Types::kTesting)
Method to get TGraph object with the ROC curve.
void Show()
Method to print the results in stdout.
Bool_t IsMethod(TString methodname, TString methodtitle)
Method to check if method was booked.
std::map< UInt_t, std::vector< std::tuple< Float_t, Float_t, Bool_t > > > fMvaTest
ROCCurve * GetROC(UInt_t iClass=0, TMVA::Types::ETreeType type=TMVA::Types::kTesting)
Method to get TMVA::ROCCurve Object.
ClassificationResult & operator=(const ClassificationResult &r)
std::map< UInt_t, std::vector< std::tuple< Float_t, Float_t, Bool_t > > > fMvaTrain
std::vector< ClassificationResult > fResults
Classification(DataLoader *loader, TFile *file, TString options)
Contructor to create a two class classifier.
Double_t GetROCIntegral(TString methodname, TString methodtitle, UInt_t iClass=0)
Method to get ROC-Integral value from mvas.
virtual void Test()
Perform test evaluation in all booked methods.
TString GetMethodOptions(TString methodname, TString methodtitle)
return the options for the booked method.
MethodBase * GetMethod(TString methodname, TString methodtitle)
Return a TMVA::MethodBase object.
virtual void TrainMethod(TString methodname, TString methodtitle)
Lets train an specific ml method.
Bool_t HasMethodObject(TString methodname, TString methodtitle, Int_t &index)
Allows to check if the TMVA::MethodBase was created and return the index in the vector.
std::vector< ClassificationResult > & GetResults()
return the the vector of TMVA::Experimental::ClassificationResult objects.
std::vector< IMethod * > fIMethods
virtual void Train()
Method to train all booked ml methods.
virtual void Evaluate()
Method to perform Train/Test over all ml method booked.
Types::EAnalysisType fAnalysisType
vector of objects with booked methods
TMVA::ROCCurve * GetROC(TMVA::MethodBase *method, UInt_t iClass=0, TMVA::Types::ETreeType type=TMVA::Types::kTesting)
Method to get TMVA::ROCCurve Object.
Bool_t IsCutsMethod(TMVA::MethodBase *method)
Allows to check if the ml method is a Cuts method.
void CopyFrom(TDirectory *src, TFile *file)
virtual void TestMethod(TString methodname, TString methodtitle)
Lets perform test an specific ml method.
Virtual base Class for all MVA method.
Definition: MethodBase.h:111
class to storage options for the differents methods
Definition: OptionMap.h:36
T GetValue(const TString &key)
Definition: OptionMap.h:135
EAnalysisType
Definition: Types.h:127
@ kTesting
Definition: Types.h:145
Mother of all ROOT objects.
Definition: TObject.h:37
Basic string class.
Definition: TString.h:131
create variable transformations
Definition: file.py:1