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TMVAMinimalClassification.C File Reference
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Detailed Description

View in nbviewer Open in SWAN Minimal self-contained example for setting up TMVA with binary classification.

This is intended as a simple foundation to build on. It assumes you are familiar with TMVA already. As such concepts like the Factory, the DataLoader and others are not explained. For descriptions and tutuorials use the TMVA User's Guide (https://root.cern.ch/root-user-guides-and-manuals under TMVA) or the more detailed examples provided with TMVA e.g. TMVAClassification.C.

Sets up a minimal binary classification example with two slighly overlapping 2-D gaussian distributions and trains a BDT classifier to discriminate the data.

<HEADER> DataSetInfo : [dataset] : Added class "Signal"
: Add Tree of type Signal with 1000 events
<HEADER> DataSetInfo : [dataset] : Added class "Background"
: Add Tree of type Background with 1000 events
<HEADER> Factory : Booking method: BDT
:
<HEADER> DataSetFactory : [dataset] : Number of events in input trees
:
:
: Dataset[dataset] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
: Dataset[dataset] : such that the effective (weighted) number of events in each class is the same
: Dataset[dataset] : (and equals the number of events (entries) given for class=0 )
: Dataset[dataset] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
: Dataset[dataset] : ... (note that N_j is the sum of TRAINING events
: Dataset[dataset] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 500
: Signal -- testing events : 500
: Signal -- training and testing events: 1000
: Background -- training events : 500
: Background -- testing events : 500
: Background -- training and testing events: 1000
:
<HEADER> DataSetInfo : Correlation matrix (Signal):
: ------------------------
: x y
: x: +1.000 +0.030
: y: +0.030 +1.000
: ------------------------
<HEADER> DataSetInfo : Correlation matrix (Background):
: ------------------------
: x y
: x: +1.000 -0.022
: y: -0.022 +1.000
: ------------------------
<HEADER> DataSetFactory : [dataset] :
:
<HEADER> Factory : Train all methods
<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: 1.0229 0.57835 [ 0.00044777 1.9988 ]
: y: 1.4942 0.76640 [ 0.014777 2.9933 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
<HEADER> IdTransformation : Ranking result (top variable is best ranked)
: --------------------------
: Rank : Variable : Separation
: --------------------------
: 1 : y : 5.413e-01
: 2 : x : 4.319e-02
: --------------------------
<HEADER> Factory : Train method: BDT for Classification
:
<HEADER> BDT : #events: (reweighted) sig: 500 bkg: 500
: #events: (unweighted) sig: 500 bkg: 500
: Training 800 Decision Trees ... patience please
: Elapsed time for training with 1000 events: 0.403 sec
<HEADER> BDT : [dataset] : Evaluation of BDT on training sample (1000 events)
: Elapsed time for evaluation of 1000 events: 0.0699 sec
: Creating xml weight file: dataset/weights/_BDT.weights.xml
: Creating standalone class: dataset/weights/_BDT.class.C
: out.root:/dataset/Method_BDT/BDT
<HEADER> Factory : Training finished
:
: Ranking input variables (method specific)...
<HEADER> BDT : Ranking result (top variable is best ranked)
: -----------------------------------
: Rank : Variable : Variable Importance
: -----------------------------------
: 1 : y : 5.011e-01
: 2 : x : 4.989e-01
: -----------------------------------
<HEADER> Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: dataset/weights/_BDT.weights.xml
<HEADER> Factory : Test all methods
<HEADER> Factory : Test method: BDT for Classification performance
:
<HEADER> BDT : [dataset] : Evaluation of BDT on testing sample (1000 events)
: Elapsed time for evaluation of 1000 events: 0.056 sec
<HEADER> Factory : Evaluate all methods
<HEADER> Factory : Evaluate classifier: BDT
:
<HEADER> BDT : [dataset] : Loop over test events and fill histograms with classifier response...
:
<HEADER> TFHandler_BDT : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: x: 1.0136 0.57754 [ 0.0011208 1.9999 ]
: y: 1.4938 0.75135 [ 0.0054384 2.9981 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset BDT : 0.870
: -------------------------------------------------------------------------------------------------------------------
:
: 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 BDT : 0.495 (0.675) 0.622 (0.754) 0.794 (0.908)
: -------------------------------------------------------------------------------------------------------------------
:
<HEADER> Dataset:dataset : Created tree 'TestTree' with 1000 events
:
<HEADER> Dataset:dataset : Created tree 'TrainTree' with 1000 events
:
<HEADER> Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
#include "TMVA/DataLoader.h"
#include "TMVA/Factory.h"
#include "TFile.h"
#include "TString.h"
#include "TTree.h"
//
// Helper function to generate 2-D gaussian data points and fill to a ROOT
// TTree.
//
// Arguments:
// nPoints Number of points to generate.
// offset Mean of the generated numbers
// scale Standard deviation of the generated numbers.
// seed Seed for random number generator. Use `seed=0` for random
// seed.
// Returns a TTree ready to be used as input to TMVA.
//
TTree *genTree(Int_t nPoints, Double_t offset, Double_t scale, UInt_t seed = 100)
{
TRandom rng(seed);
Double_t x = 0;
Double_t y = 0;
TTree *data = new TTree();
data->Branch("x", &x, "x/D");
data->Branch("y", &y, "y/D");
for (Int_t n = 0; n < nPoints; ++n) {
x = rng.Rndm() * scale;
y = offset + rng.Rndm() * scale;
data->Fill();
}
// Important: Disconnects the tree from the memory locations of x and y.
data->ResetBranchAddresses();
return data;
}
//
// Minimal setup for perfroming binary classification in TMVA.
//
// Modify the setup to your liking and run with
// `root -l -b -q TMVAMinimalClassification.C`.
// This will generate an output file "out.root" that can be viewed with
// `root -l -e 'TMVA::TMVAGui("out.root")'`.
//
void TMVAMinimalClassification()
{
TString outputFilename = "out.root";
TFile *outFile = new TFile(outputFilename, "RECREATE");
// Data generatration
TTree *signalTree = genTree(1000, 0.0, 2.0, 100);
TTree *backgroundTree = genTree(1000, 1.0, 2.0, 101);
TString factoryOptions = "AnalysisType=Classification";
TMVA::Factory factory{"", outFile, factoryOptions};
TMVA::DataLoader dataloader{"dataset"};
// Data specification
dataloader.AddVariable("x", 'D');
dataloader.AddVariable("y", 'D');
dataloader.AddSignalTree(signalTree, 1.0);
dataloader.AddBackgroundTree(backgroundTree, 1.0);
TCut signalCut = "";
TCut backgroundCut = "";
TString datasetOptions = "SplitMode=Random";
dataloader.PrepareTrainingAndTestTree(signalCut, backgroundCut, datasetOptions);
// Method specification
TString methodOptions = "";
factory.BookMethod(&dataloader, TMVA::Types::kBDT, "BDT", methodOptions);
// Training and Evaluation
factory.TrainAllMethods();
factory.TestAllMethods();
factory.EvaluateAllMethods();
// Clean up
outFile->Close();
delete outFile;
delete signalTree;
delete backgroundTree;
}
Int_t
int Int_t
Definition: RtypesCore.h:41
UInt_t
unsigned int UInt_t
Definition: RtypesCore.h:42
Double_t
double Double_t
Definition: RtypesCore.h:55
TCut
A specialized string object used for TTree selections.
Definition: TCut.h:25
TFile
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:48
TFile::Close
virtual void Close(Option_t *option="")
Close a file.
Definition: TFile.cxx:914
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:470
TMVA::Factory
This is the main MVA steering class.
Definition: Factory.h:81
TMVA::Types::kBDT
@ kBDT
Definition: Types.h:88
TRandom
This is the base class for the ROOT Random number generators.
Definition: TRandom.h:27
TString
Basic string class.
Definition: TString.h:131
TTree
A TTree represents a columnar dataset.
Definition: TTree.h:71
TTree::Fill
virtual Int_t Fill()
Fill all branches.
Definition: TTree.cxx:4419
TTree::ResetBranchAddresses
virtual void ResetBranchAddresses()
Tell all of our branches to drop their current objects and allocate new ones.
Definition: TTree.cxx:7781
TTree::Branch
virtual Int_t Branch(TCollection *list, Int_t bufsize=32000, Int_t splitlevel=99, const char *name="")
Create one branch for each element in the collection.
Definition: TTree.cxx:1741
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
Author
Kim Albertsson

Definition in file TMVAMinimalClassification.C.