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RMethodBase.cxx
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1// @(#)root/tmva/rmva $Id$
2// Author: Omar Zapata,Lorenzo Moneta, Sergei Gleyzer 2015
3
4
5/**********************************************************************************
6 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
7 * Package: TMVA *
8 * Class : RMethodBase *
9 * *
10 * Description: *
11 * Virtual base class for all MVA method based on ROOTR *
12 * *
13 **********************************************************************************/
14
15#include<TMVA/RMethodBase.h>
16#include <TMVA/DataSetInfo.h>
17#include<TApplication.h>
18using namespace TMVA;
19
21
22//_______________________________________________________________________
24 Types::EMVA methodType,
25 const TString &methodTitle,
26 DataSetInfo &dsi,
27 const TString &theOption , ROOT::R::TRInterface &_r): MethodBase(jobName, methodType, methodTitle, dsi, theOption),
28 r(_r)
29{
30 LoadData();
31}
32
33//_______________________________________________________________________
35 DataSetInfo &dsi,
36 const TString &weightFile,ROOT::R::TRInterface &_r): MethodBase(methodType, dsi, weightFile),
37 r(_r)
38{
39 LoadData();
40}
41
42//_______________________________________________________________________
44{
45 ///////////////////////////
46 //Loading Training Data //
47 ///////////////////////////
48 const UInt_t nvar = DataInfo().GetNVariables();
49
50 const UInt_t ntrains = Data()->GetNTrainingEvents();
51
52 //array of columns for every var to create a dataframe for training
53 std::vector<std::vector<Float_t> > fArrayTrain(nvar);
54// Data()->SetCurrentEvent(1);
55// Data()->SetCurrentType(Types::ETreeType::kTraining);
56
57 fWeightTrain.ResizeTo(ntrains);
58 for (UInt_t j = 0; j < ntrains; j++) {
60// const Event *ev=Data()->GetEvent( j );
61 //creating array with class type(signal or background) for factor required
62 if (ev->GetClass() == Types::kSignal) fFactorTrain.push_back("signal");
63 else fFactorTrain.push_back("background");
64
65 fWeightTrain[j] = ev->GetWeight();
66
67 //filling vector of columns for training
68 for (UInt_t i = 0; i < nvar; i++) {
69 fArrayTrain[i].push_back(ev->GetValue(i));
70 }
71
72 }
73 for (UInt_t i = 0; i < nvar; i++) {
74 fDfTrain[DataInfo().GetListOfVariables()[i].Data()] = fArrayTrain[i];
75 }
76 ////////////////////////
77 //Loading Test Data //
78 ////////////////////////
79
80 const UInt_t ntests = Data()->GetNTestEvents();
81 const UInt_t nspectators = DataInfo().GetNSpectators(kTRUE);
82
83 //array of columns for every var to create a dataframe for testing
84 std::vector<std::vector<Float_t> > fArrayTest(nvar);
85 //array of columns for every spectator to create a dataframe for testing
86 std::vector<std::vector<Float_t> > fArraySpectators(nvar);
87 fWeightTest.ResizeTo(ntests);
88// Data()->SetCurrentType(Types::ETreeType::kTesting);
89 for (UInt_t j = 0; j < ntests; j++) {
91// const Event *ev=Data()->GetEvent(j);
92 //creating array with class type(signal or background) for factor required
93 if (ev->GetClass() == Types::kSignal) fFactorTest.push_back("signal");
94 else fFactorTest.push_back("background");
95
96 fWeightTest[j] = ev->GetWeight();
97
98 for (UInt_t i = 0; i < nvar; i++) {
99 fArrayTest[i].push_back(ev->GetValue(i));
100 }
101 for (UInt_t i = 0; i < nspectators; i++) {
102 fArraySpectators[i].push_back(ev->GetSpectator(i));
103 }
104 }
105 for (UInt_t i = 0; i < nvar; i++) {
106 fDfTest[DataInfo().GetListOfVariables()[i].Data()] = fArrayTest[i];
107 }
108 for (UInt_t i = 0; i < nspectators; i++) {
109 fDfSpectators[DataInfo().GetSpectatorInfo(i).GetLabel().Data()] = fArraySpectators[i];
110 }
111
112}
constexpr Bool_t kTRUE
Definition RtypesCore.h:100
#define ClassImp(name)
Definition Rtypes.h:377
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t r
ROOT R was implemented using the R Project library and the modules Rcpp and RInside
Class that contains all the data information.
Definition DataSetInfo.h:62
UInt_t GetNVariables() const
UInt_t GetNSpectators(bool all=kTRUE) const
std::vector< TString > GetListOfVariables() const
returns list of variables
VariableInfo & GetSpectatorInfo(Int_t i)
Long64_t GetNTestEvents() const
Definition DataSet.h:69
const Event * GetEvent() const
returns event without transformations
Definition DataSet.cxx:202
Long64_t GetNTrainingEvents() const
Definition DataSet.h:68
Float_t GetValue(UInt_t ivar) const
return value of i'th variable
Definition Event.cxx:236
Double_t GetWeight() const
return the event weight - depending on whether the flag IgnoreNegWeightsInTraining is or not.
Definition Event.cxx:389
Float_t GetSpectator(UInt_t ivar) const
return spectator content
Definition Event.cxx:261
UInt_t GetClass() const
Definition Event.h:86
Virtual base Class for all MVA method.
Definition MethodBase.h:111
DataSetInfo & DataInfo() const
Definition MethodBase.h:410
DataSet * Data() const
Definition MethodBase.h:409
std::vector< std::string > fFactorTrain
Definition RMethodBase.h:95
ROOT::R::TRDataFrame fDfTrain
Definition RMethodBase.h:91
RMethodBase(const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="", ROOT::R::TRInterface &_r=ROOT::R::TRInterface::Instance())
TVectorD fWeightTrain
Definition RMethodBase.h:93
ROOT::R::TRDataFrame fDfTest
Definition RMethodBase.h:92
TVectorD fWeightTest
Definition RMethodBase.h:94
std::vector< std::string > fFactorTest
Definition RMethodBase.h:96
ROOT::R::TRDataFrame fDfSpectators
Definition RMethodBase.h:97
@ kSignal
Never change this number - it is elsewhere assumed to be zero !
Definition Types.h:135
@ kTraining
Definition Types.h:143
const TString & GetLabel() const
Basic string class.
Definition TString.h:139
const char * Data() const
Definition TString.h:378
TVectorT< Element > & ResizeTo(Int_t lwb, Int_t upb)
Resize the vector to [lwb:upb] .
Definition TVectorT.cxx:294
create variable transformations