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TMVACrossValidation.C File Reference
Tutorials » TMVA tutorials

Detailed Description

View in nbviewer Open in SWAN This macro provides an example of how to use TMVA for k-folds cross evaluation.

As input data is used a toy-MC sample consisting of two guassian distributions.

The output file "TMVA.root" can be analysed with the use of dedicated macros (simply say: root -l <macro.C>), which can be conveniently invoked through a GUI that will appear at the end of the run of this macro. Launch the GUI via the command:

root -l -e 'TMVA::TMVAGui("TMVA.root")'
e
#define e(i)
Definition RSha256.hxx:103
l
auto * l
Definition textangle.C:4

Cross Evaluation

Cross evaluation is a special case of k-folds cross validation where the splitting into k folds is computed deterministically. This ensures that the a given event will always end up in the same fold.

In addition all resulting classifiers are saved and can be applied to new data using MethodCrossValidation. One requirement for this to work is a splitting function that is evaluated for each event to determine into what fold it goes (for training/evaluation) or to what classifier (for application).

Split Expression

Cross evaluation uses a deterministic split to partition the data into folds called the split expression. The expression can be any valid TFormula as long as all parts used are defined.

For each event the split expression is evaluated to a number and the event is put in the fold corresponding to that number.

It is recommended to always use int([NumFolds]) at the end of the expression.

The split expression has access to all spectators and variables defined in the dataloader. Additionally, the number of folds in the split can be accessed with NumFolds (or numFolds).

Example

"int(fabs([eventID]))%int([NumFolds])"
  • Project : TMVA - a ROOT-integrated toolkit for multivariate data analysis
  • Package : TMVA
  • Root Macro: TMVACrossValidation
DataSetInfo : [dataset] : Added class "Signal"
: Add Tree of type Signal with 1000 events
DataSetInfo : [dataset] : Added class "Background"
: Add Tree of type Background with 1000 events
<HEADER> Factory : You are running ROOT Version: 6.24/09, Sep 29, 2022
:
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: _/ _| _| _| _| _|
:
: ___________TMVA Version 4.2.1, Feb 5, 2015
:
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree
<HEADER> DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 999
: Signal -- testing events : 1
: Signal -- training and testing events: 1000
: Background -- training events : 999
: Background -- testing events : 1
: Background -- training and testing events: 1000
:
<HEADER> DataSetInfo : Correlation matrix (Signal):
: ------------------------
: x y
: x: +1.000 +0.075
: y: +0.075 +1.000
: ------------------------
<HEADER> DataSetInfo : Correlation matrix (Background):
: ------------------------
: x y
: x: +1.000 +0.020
: y: +0.020 +1.000
: ------------------------
<HEADER> DataSetFactory : [dataset] :
:
:
:
: ========================================
: ========================================
:
<HEADER> Factory : Booking method: BDTG_fold1
:
<HEADER> BDTG_fold1 : #events: (reweighted) sig: 500 bkg: 500
: #events: (unweighted) sig: 500 bkg: 500
: Training 100 Decision Trees ... patience please
: Elapsed time for training with 1000 events: 0.0434 sec
<HEADER> BDTG_fold1 : [dataset] : Evaluation of BDTG_fold1 on training sample (1000 events)
: Elapsed time for evaluation of 1000 events: 0.00343 sec
: Creating xml weight file: dataset/weights/TMVACrossValidation_BDTG_fold1.weights.xml
: Creating standalone class: dataset/weights/TMVACrossValidation_BDTG_fold1.class.C
<HEADER> Factory : Test all methods
<HEADER> Factory : Test method: BDTG_fold1 for Classification performance
:
<HEADER> BDTG_fold1 : [dataset] : Evaluation of BDTG_fold1 on testing sample (998 events)
: Elapsed time for evaluation of 998 events: 0.0034 sec
<HEADER> Factory : Evaluate all methods
<HEADER> Factory : Evaluate classifier: BDTG_fold1
:
<HEADER> BDTG_fold1 : [dataset] : Loop over test events and fill histograms with classifier response...
:
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset BDTG_fold1 : 0.973
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset BDTG_fold1 : 0.575 (0.725) 0.947 (0.933) 0.981 (0.980)
: -------------------------------------------------------------------------------------------------------------------
:
<HEADER> Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
<HEADER> Factory : Booking method: BDTG_fold2
:
<HEADER> BDTG_fold2 : #events: (reweighted) sig: 499 bkg: 499
: #events: (unweighted) sig: 499 bkg: 499
: Training 100 Decision Trees ... patience please
: Elapsed time for training with 998 events: 0.0431 sec
<HEADER> BDTG_fold2 : [dataset] : Evaluation of BDTG_fold2 on training sample (998 events)
: Elapsed time for evaluation of 998 events: 0.00352 sec
: Creating xml weight file: dataset/weights/TMVACrossValidation_BDTG_fold2.weights.xml
: Creating standalone class: dataset/weights/TMVACrossValidation_BDTG_fold2.class.C
<HEADER> Factory : Test all methods
<HEADER> Factory : Test method: BDTG_fold2 for Classification performance
:
<HEADER> BDTG_fold2 : [dataset] : Evaluation of BDTG_fold2 on testing sample (1000 events)
: Elapsed time for evaluation of 1000 events: 0.00357 sec
<HEADER> Factory : Evaluate all methods
<HEADER> Factory : Evaluate classifier: BDTG_fold2
:
<HEADER> BDTG_fold2 : [dataset] : Loop over test events and fill histograms with classifier response...
:
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset BDTG_fold2 : 0.961
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset BDTG_fold2 : 0.646 (0.696) 0.868 (0.930) 0.975 (0.976)
: -------------------------------------------------------------------------------------------------------------------
:
<HEADER> Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
<HEADER> Factory : Booking method: BDTG
:
: Reading weightfile: dataset/weights/TMVACrossValidation_BDTG_fold1.weights.xml
: Reading weight file: dataset/weights/TMVACrossValidation_BDTG_fold1.weights.xml
: Reading weightfile: dataset/weights/TMVACrossValidation_BDTG_fold2.weights.xml
: Reading weight file: dataset/weights/TMVACrossValidation_BDTG_fold2.weights.xml
:
:
: ========================================
: ========================================
:
<HEADER> Factory : Booking method: Fisher_fold1
:
<HEADER> Fisher_fold1 : Results for Fisher coefficients:
: -----------------------
: Variable: Coefficient:
: -----------------------
: x: +0.449
: y: +0.436
: (offset): +0.019
: -----------------------
: Elapsed time for training with 1000 events: 0.000355 sec
<HEADER> Fisher_fold1 : [dataset] : Evaluation of Fisher_fold1 on training sample (1000 events)
: Elapsed time for evaluation of 1000 events: 7.1e-05 sec
: Creating xml weight file: dataset/weights/TMVACrossValidation_Fisher_fold1.weights.xml
: Creating standalone class: dataset/weights/TMVACrossValidation_Fisher_fold1.class.C
<HEADER> Factory : Test all methods
<HEADER> Factory : Test method: Fisher_fold1 for Classification performance
:
<HEADER> Fisher_fold1 : [dataset] : Evaluation of Fisher_fold1 on testing sample (998 events)
: Elapsed time for evaluation of 998 events: 0.000105 sec
<HEADER> Factory : Evaluate all methods
<HEADER> Factory : Evaluate classifier: Fisher_fold1
:
<HEADER> Fisher_fold1 : [dataset] : Loop over test events and fill histograms with classifier response...
:
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset Fisher_fold1 : 0.976
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset Fisher_fold1 : 0.660 (0.665) 0.952 (0.923) 0.986 (0.985)
: -------------------------------------------------------------------------------------------------------------------
:
<HEADER> Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
<HEADER> Factory : Booking method: Fisher_fold2
:
<HEADER> Fisher_fold2 : Results for Fisher coefficients:
: -----------------------
: Variable: Coefficient:
: -----------------------
: x: +0.501
: y: +0.467
: (offset): -0.000
: -----------------------
: Elapsed time for training with 998 events: 0.000305 sec
<HEADER> Fisher_fold2 : [dataset] : Evaluation of Fisher_fold2 on training sample (998 events)
: Elapsed time for evaluation of 998 events: 7.1e-05 sec
: Creating xml weight file: dataset/weights/TMVACrossValidation_Fisher_fold2.weights.xml
: Creating standalone class: dataset/weights/TMVACrossValidation_Fisher_fold2.class.C
<HEADER> Factory : Test all methods
<HEADER> Factory : Test method: Fisher_fold2 for Classification performance
:
<HEADER> Fisher_fold2 : [dataset] : Evaluation of Fisher_fold2 on testing sample (1000 events)
: Elapsed time for evaluation of 1000 events: 0.000118 sec
<HEADER> Factory : Evaluate all methods
<HEADER> Factory : Evaluate classifier: Fisher_fold2
:
<HEADER> Fisher_fold2 : [dataset] : Loop over test events and fill histograms with classifier response...
:
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset Fisher_fold2 : 0.966
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset Fisher_fold2 : 0.655 (0.645) 0.900 (0.928) 0.975 (0.977)
: -------------------------------------------------------------------------------------------------------------------
:
<HEADER> Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
<HEADER> Factory : Booking method: Fisher
:
: Reading weightfile: dataset/weights/TMVACrossValidation_Fisher_fold1.weights.xml
: Reading weight file: dataset/weights/TMVACrossValidation_Fisher_fold1.weights.xml
: Reading weightfile: dataset/weights/TMVACrossValidation_Fisher_fold2.weights.xml
: Reading weight file: dataset/weights/TMVACrossValidation_Fisher_fold2.weights.xml
:
:
: ========================================
: Folds processed for all methods, evaluating.
: ========================================
:
<HEADER> Factory : [dataset] : Create Transformation "I" with events from all classes.
:
<HEADER> : Transformation, Variable selection :
: Input : variable 'x' <---> Output : variable 'x'
: Input : variable 'y' <---> Output : variable 'y'
<HEADER> TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: x: -0.014284 1.4061 [ -4.1075 4.0969 ]
: y: -0.0066370 1.4204 [ -4.8520 4.0761 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
<HEADER> IdTransformation : Ranking result (top variable is best ranked)
: --------------------------
: Rank : Variable : Separation
: --------------------------
: 1 : x : 5.429e-01
: 2 : y : 5.230e-01
: --------------------------
: Elapsed time for training with 1998 events: 5.01e-06 sec
<HEADER> BDTG : [dataset] : Evaluation of BDTG on training sample (1998 events)
: Elapsed time for evaluation of 1998 events: 0.00641 sec
: Creating xml weight file: dataset/weights/TMVACrossValidation_BDTG.weights.xml
: Creating standalone class: dataset/weights/TMVACrossValidation_BDTG.class.C
<WARNING> <WARNING> : MakeClassSpecificHeader not implemented for CrossValidation
<WARNING> <WARNING> : MakeClassSpecific not implemented for CrossValidation
: Elapsed time for training with 1998 events: 4.05e-06 sec
<HEADER> Fisher : [dataset] : Evaluation of Fisher on training sample (1998 events)
: Elapsed time for evaluation of 1998 events: 0.00032 sec
: Creating xml weight file: dataset/weights/TMVACrossValidation_Fisher.weights.xml
: Creating standalone class: dataset/weights/TMVACrossValidation_Fisher.class.C
<WARNING> <WARNING> : MakeClassSpecificHeader not implemented for CrossValidation
<WARNING> <WARNING> : MakeClassSpecific not implemented for CrossValidation
<HEADER> Factory : Test all methods
<HEADER> Factory : Test method: BDTG for Classification performance
:
<HEADER> BDTG : [dataset] : Evaluation of BDTG on testing sample (1998 events)
: Elapsed time for evaluation of 1998 events: 0.00631 sec
<HEADER> Factory : Test method: Fisher for Classification performance
:
<HEADER> Fisher : [dataset] : Evaluation of Fisher on testing sample (1998 events)
: Elapsed time for evaluation of 1998 events: 0.000308 sec
<HEADER> Factory : Evaluate all methods
<HEADER> Factory : Evaluate classifier: BDTG
:
<HEADER> BDTG : [dataset] : Loop over test events and fill histograms with classifier response...
:
<HEADER> TFHandler_BDTG : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: x: -0.014284 1.4061 [ -4.1075 4.0969 ]
: y: -0.0066370 1.4204 [ -4.8520 4.0761 ]
: -----------------------------------------------------------
<HEADER> Factory : Evaluate classifier: Fisher
:
<HEADER> Fisher : [dataset] : Loop over test events and fill histograms with classifier response...
:
<HEADER> TFHandler_Fisher : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: x: -0.014284 1.4061 [ -4.1075 4.0969 ]
: y: -0.0066370 1.4204 [ -4.8520 4.0761 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset Fisher : 0.971
: dataset BDTG : 0.965
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset Fisher : 0.665 (0.665) 0.922 (0.922) 0.980 (0.980)
: dataset BDTG : 0.617 (0.617) 0.914 (0.914) 0.974 (0.974)
: -------------------------------------------------------------------------------------------------------------------
:
<HEADER> Dataset:dataset : Created tree 'TestTree' with 1998 events
:
<HEADER> Dataset:dataset : Created tree 'TrainTree' with 1998 events
:
<HEADER> Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
: Evaluation done.
Summary for method BDT
Fold 0: ROC int: 0.972504, BkgEff@SigEff=0.3: 0.981
Fold 1: ROC int: 0.96115, BkgEff@SigEff=0.3: 0.975
Summary for method Fisher
Fold 0: ROC int: 0.976137, BkgEff@SigEff=0.3: 0.986
Fold 1: ROC int: 0.96584, BkgEff@SigEff=0.3: 0.975
==> Wrote root file: TMVA.root
==> TMVACrossValidation is done!
(int) 0
#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 "TMVA/CrossValidation.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Factory.h"
#include "TMVA/Tools.h"
#include "TMVA/TMVAGui.h"
// Helper function to load data into TTrees.
TTree *genTree(Int_t nPoints, Double_t offset, Double_t scale, UInt_t seed = 100)
{
TRandom3 rng(seed);
Float_t x = 0;
Float_t y = 0;
UInt_t eventID = 0;
TTree *data = new TTree();
data->Branch("x", &x, "x/F");
data->Branch("y", &y, "y/F");
data->Branch("eventID", &eventID, "eventID/I");
for (Int_t n = 0; n < nPoints; ++n) {
x = rng.Gaus(offset, scale);
y = rng.Gaus(offset, scale);
// For our simple example it is enough that the id's are uniformly
// distributed and independent of the data.
++eventID;
data->Fill();
}
// Important: Disconnects the tree from the memory locations of x and y.
data->ResetBranchAddresses();
return data;
}
int TMVACrossValidation(bool useRandomSplitting = false)
{
// This loads the library
TMVA::Tools::Instance();
// --------------------------------------------------------------------------
// Load the data into TTrees. If you load data from file you can use a
// variant of
// ```
// TString filename = "/path/to/file";
// TFile * input = TFile::Open( filename );
// TTree * signalTree = (TTree*)input->Get("TreeName");
// ```
TTree *sigTree = genTree(1000, 1.0, 1.0, 100);
TTree *bkgTree = genTree(1000, -1.0, 1.0, 101);
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName("TMVA.root");
TFile *outputFile = TFile::Open(outfileName, "RECREATE");
// DataLoader definitions; We declare variables in the tree so that TMVA can
// find them. For more information see TMVAClassification tutorial.
TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
// Data variables
dataloader->AddVariable("x", 'F');
dataloader->AddVariable("y", 'F');
// Spectator used for split
dataloader->AddSpectator("eventID", 'I');
// NOTE: Currently TMVA treats all input variables, spectators etc as
// floats. Thus, if the absolute value of the input is too large
// there can be precision loss. This can especially be a problem for
// cross validation with large event numbers.
// A workaround is to define your splitting variable as:
// `dataloader->AddSpectator("eventID := eventID % 4096", 'I');`
// where 4096 should be a number much larger than the number of folds
// you intend to run with.
// Attaches the trees so they can be read from
dataloader->AddSignalTree(sigTree, 1.0);
dataloader->AddBackgroundTree(bkgTree, 1.0);
// The CV mechanism of TMVA splits up the training set into several folds.
// The test set is currently left unused. The `nTest_ClassName=1` assigns
// one event to the the test set for each class and puts the rest in the
// training set. A value of 0 is a special value and would split the
// datasets 50 / 50.
dataloader->PrepareTrainingAndTestTree("", "",
"nTest_Signal=1"
":nTest_Background=1"
":SplitMode=Random"
":NormMode=NumEvents"
":!V");
// --------------------------------------------------------------------------
//
// This sets up a CrossValidation class (which wraps a TMVA::Factory
// internally) for 2-fold cross validation.
//
// The split type can be "Random", "RandomStratified" or "Deterministic".
// For the last option, check the comment below. Random splitting randomises
// the order of events and distributes events as evenly as possible.
// RandomStratified applies the same logic but distributes events within a
// class as evenly as possible over the folds.
//
UInt_t numFolds = 2;
TString analysisType = "Classification";
TString splitType = (useRandomSplitting) ? "Random" : "Deterministic";
//
// One can also use a custom splitting function for producing the folds.
// The example uses a dataset spectator `eventID`.
//
// The idea here is that eventID should be an event number that is integral,
// random and independent of the data, generated only once. This last
// property ensures that if a calibration is changed the same event will
// still be assigned the same fold.
//
// This can be used to use the cross validated classifiers in application,
// a technique that can simplify statistical analysis.
//
// If you want to run TMVACrossValidationApplication, make sure you have
// run this tutorial with Deterministic splitting type, i.e.
// with the option useRandomSPlitting = false
//
TString splitExpr = (!useRandomSplitting) ? "int(fabs([eventID]))%int([NumFolds])" : "";
TString cvOptions = Form("!V"
":!Silent"
":ModelPersistence"
":AnalysisType=%s"
":SplitType=%s"
":NumFolds=%i"
":SplitExpr=%s",
analysisType.Data(), splitType.Data(), numFolds,
splitExpr.Data());
TMVA::CrossValidation cv{"TMVACrossValidation", dataloader, outputFile, cvOptions};
// --------------------------------------------------------------------------
//
// Books a method to use for evaluation
//
cv.BookMethod(TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=100:MinNodeSize=2.5%:BoostType=Grad"
":NegWeightTreatment=Pray:Shrinkage=0.10:nCuts=20"
":MaxDepth=2");
cv.BookMethod(TMVA::Types::kFisher, "Fisher",
"!H:!V:Fisher:VarTransform=None");
// --------------------------------------------------------------------------
//
// Train, test and evaluate the booked methods.
// Evaluates the booked methods once for each fold and aggregates the result
// in the specified output file.
//
cv.Evaluate();
// --------------------------------------------------------------------------
//
// Process some output programatically, printing the ROC score for each
// booked method.
//
size_t iMethod = 0;
for (auto && result : cv.GetResults()) {
std::cout << "Summary for method " << cv.GetMethods()[iMethod++].GetValue<TString>("MethodName")
<< std::endl;
for (UInt_t iFold = 0; iFold<cv.GetNumFolds(); ++iFold) {
std::cout << "\tFold " << iFold << ": "
<< "ROC int: " << result.GetROCValues()[iFold]
<< ", "
<< "BkgEff@SigEff=0.3: " << result.GetEff30Values()[iFold]
<< std::endl;
}
}
// --------------------------------------------------------------------------
//
// Save the output
//
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVACrossValidation is done!" << std::endl;
// --------------------------------------------------------------------------
//
// Launch the GUI for the root macros
//
if (!gROOT->IsBatch()) {
// Draw cv-specific graphs
cv.GetResults()[0].DrawAvgROCCurve(kTRUE, "Avg ROC for BDTG");
cv.GetResults()[0].DrawAvgROCCurve(kTRUE, "Avg ROC for Fisher");
// You can also use the classical gui
TMVA::TMVAGui(outfileName);
}
return 0;
}
//
// This is used if the macro is compiled. If run through ROOT with
// `root -l -b -q MACRO.C` or similar it is unused.
//
int main(int argc, char **argv)
{
TMVACrossValidation();
}
Int_t
int Int_t
Definition RtypesCore.h:45
UInt_t
unsigned int UInt_t
Definition RtypesCore.h:46
Double_t
double Double_t
Definition RtypesCore.h:59
Float_t
float Float_t
Definition RtypesCore.h:57
kTRUE
const Bool_t kTRUE
Definition RtypesCore.h:91
gROOT
#define gROOT
Definition TROOT.h:406
Form
char * Form(const char *fmt,...)
TFile
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition TFile.h:54
TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:3997
TFile::Close
void Close(Option_t *option="") override
Close a file.
Definition TFile.cxx:879
TMVA::CrossValidation
Class to perform cross validation, splitting the dataloader into folds.
Definition CrossValidation.h:124
TMVA::DataLoader::AddSignalTree
void AddSignalTree(TTree *signal, Double_t weight=1.0, Types::ETreeType treetype=Types::kMaxTreeType)
number of signal events (used to compute significance)
Definition DataLoader.cxx:370
TMVA::DataLoader::AddSpectator
void AddSpectator(const TString &expression, const TString &title="", const TString &unit="", Double_t min=0, Double_t max=0)
user inserts target in data set info
Definition DataLoader.cxx:523
TMVA::DataLoader::PrepareTrainingAndTestTree
void PrepareTrainingAndTestTree(const TCut &cut, const TString &splitOpt)
prepare the training and test trees -> same cuts for signal and background
Definition DataLoader.cxx:631
TMVA::DataLoader::AddBackgroundTree
void AddBackgroundTree(TTree *background, Double_t weight=1.0, Types::ETreeType treetype=Types::kMaxTreeType)
number of signal events (used to compute significance)
Definition DataLoader.cxx:401
TMVA::DataLoader::AddVariable
void AddVariable(const TString &expression, const TString &title, const TString &unit, char type='F', Double_t min=0, Double_t max=0)
user inserts discriminating variable in data set info
Definition DataLoader.cxx:484
TMVA::Tools::Instance
static Tools & Instance()
Definition Tools.cxx:75
TMVA::Types::kFisher
@ kFisher
Definition Types.h:84
TNamed::GetName
virtual const char * GetName() const
Returns name of object.
Definition TNamed.h:47
TRandom3
Random number generator class based on M.
Definition TRandom3.h:27
TString
Basic string class.
Definition TString.h:136
TString::Data
const char * Data() const
Definition TString.h:369
TTree
A TTree represents a columnar dataset.
Definition TTree.h:79
TTree::Fill
virtual Int_t Fill()
Fill all branches.
Definition TTree.cxx:4590
TTree::Branch
TBranch * Branch(const char *name, T *obj, Int_t bufsize=32000, Int_t splitlevel=99)
Add a new branch, and infer the data type from the type of obj being passed.
Definition TTree.h:350
TTree::ResetBranchAddresses
virtual void ResetBranchAddresses()
Tell all of our branches to drop their current objects and allocate new ones.
Definition TTree.cxx:8047
main
int main()
Definition functioncalls_main.cxx:68
y
Double_t y[n]
Definition legend1.C:17
x
Double_t x[n]
Definition legend1.C:17
n
const Int_t n
Definition legend1.C:16
TMVA::TMVAGui
void TMVAGui(const char *fName="TMVA.root", TString dataset="")
Author
Kim Albertsson (adapted from code originally by Andreas Hoecker)

Definition in file TMVACrossValidation.C.