This macro provides a simple example on how to use the trained regression MVAs within an analysis module
Author: Andreas Hoecker
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, March 19, 2024 at 07:21 PM.
%%cpp -d
#include <cstdlib>
#include <vector>
#include <iostream>
#include <map>
#include <string>
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TStopwatch.h"
#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
using namespace TMVA;
Arguments are defined.
TString myMethodList = "";
This loads the library
TMVA::Tools::Instance();
Default MVA methods to be trained + tested
std::map<std::string,int> Use;
--- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 1;
Use["KNN"] = 1;
--- Linear Discriminant Analysis
Use["LD"] = 1;
--- Function Discriminant analysis
Use["FDA_GA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
--- Neural Network
Use["MLP"] = 0;
Deep neural network
#ifdef R__HAS_TMVAGPU
Use["DNN_GPU"] = 1;
Use["DNN_CPU"] = 0;
#else
Use["DNN_GPU"] = 0;
#ifdef R__HAS_TMVACPU
Use["DNN_CPU"] = 1;
#else
Use["DNN_CPU"] = 0;
#endif
#endif
Unbalanced braces. This cell was not processed.
--- Support Vector Machine
Use["SVM"] = 0;
--- Boosted Decision Trees
Use["BDT"] = 0;
Use["BDTG"] = 1;
std::cout << std::endl;
std::cout << "==> Start TMVARegressionApplication" << std::endl;
==> Start TMVARegressionApplication
Select methods (don't look at this code - not of interest)
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
--- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
Create a set of variables and declare them to the reader
Float_t var1, var2;
reader->AddVariable( "var1", &var1 );
reader->AddVariable( "var2", &var2 );
Spectator variables declared in the training have to be added to the reader, too
Float_t spec1,spec2;
reader->AddSpectator( "spec1:=var1*2", &spec1 );
reader->AddSpectator( "spec2:=var1*3", &spec2 );
--- Book the MVA methods
TString dir = "datasetreg/weights/";
TString prefix = "TMVARegression";
Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = it->first + " method";
TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
reader->BookMVA( methodName, weightfile );
}
}
: Booking "BDTG method" of type "BDT" from datasetreg/weights/TMVARegression_BDTG.weights.xml. : Reading weight file: datasetreg/weights/TMVARegression_BDTG.weights.xml <HEADER> DataSetInfo : [Default] : Added class "Regression" : Booked classifier "BDTG" of type: "BDT" : Booking "KNN method" of type "KNN" from datasetreg/weights/TMVARegression_KNN.weights.xml. : Reading weight file: datasetreg/weights/TMVARegression_KNN.weights.xml : Creating kd-tree with 1000 events : Computing scale factor for 1d distributions: (ifrac, bottom, top) = (80%, 10%, 90%) <HEADER> ModulekNN : Optimizing tree for 2 variables with 1000 values : <Fill> Class 1 has 1000 events : Booked classifier "KNN" of type: "KNN" : Booking "LD method" of type "LD" from datasetreg/weights/TMVARegression_LD.weights.xml. : Reading weight file: datasetreg/weights/TMVARegression_LD.weights.xml : Booked classifier "LD" of type: "LD" : Booking "PDEFoam method" of type "PDEFoam" from datasetreg/weights/TMVARegression_PDEFoam.weights.xml. : Reading weight file: datasetreg/weights/TMVARegression_PDEFoam.weights.xml : Read foams from file: datasetreg/weights/TMVARegression_PDEFoam.weights_foams.root : Booked classifier "PDEFoam" of type: "PDEFoam"
Book output histograms
TH1* hists[100];
Int_t nhists = -1;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
TH1* h = new TH1F( it->first.c_str(), TString(it->first) + " method", 100, -100, 600 );
if (it->second) hists[++nhists] = h;
}
nhists++;
Prepare input tree (this must be replaced by your data source) in this example, there is a toy tree with signal and one with background events we'll later on use only the "signal" events for the test in this example.
TFile *input(0);
TString fname = "./tmva_reg_example.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
else {
TFile::SetCacheFileDir(".");
input = TFile::Open("http://root.cern/files/tmva_reg_example.root", "CACHEREAD"); // if not: download from ROOT server
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVARegressionApp : Using input file: " << input->GetName() << std::endl;
--- TMVARegressionApp : Using input file: ./files/tmva_reg_example.root
Info in <TFile::OpenFromCache>: using local cache copy of http://root.cern/files/tmva_reg_example.root [./files/tmva_reg_example.root]
--- Event loop
Prepare the tree
but of course you can use different ones and copy the values inside the event loop
TTree* theTree = (TTree*)input->Get("TreeR");
std::cout << "--- Select signal sample" << std::endl;
theTree->SetBranchAddress( "var1", &var1 );
theTree->SetBranchAddress( "var2", &var2 );
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) {
std::cout << "--- ... Processing event: " << ievt << std::endl;
}
theTree->GetEntry(ievt);
// Retrieve the MVA target values (regression outputs) and fill into histograms
// NOTE: EvaluateRegression(..) returns a vector for multi-target regression
for (Int_t ih=0; ih<nhists; ih++) {
TString title = hists[ih]->GetTitle();
Float_t val = (reader->EvaluateRegression( title ))[0];
hists[ih]->Fill( val );
}
}
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
--- Select signal sample --- Processing: 10000 events --- ... Processing event: 0 : Rebuilding Dataset Default --- ... Processing event: 1000 --- ... Processing event: 2000 --- ... Processing event: 3000 --- ... Processing event: 4000 --- ... Processing event: 5000 --- ... Processing event: 6000 --- ... Processing event: 7000 --- ... Processing event: 8000 --- ... Processing event: 9000 --- End of event loop: Real time 0:00:04, CP time 4.420
--- Write histograms
TFile *target = new TFile( "TMVARegApp.root","RECREATE" );
for (Int_t ih=0; ih<nhists; ih++) hists[ih]->Write();
target->Close();
std::cout << "--- Created root file: \"" << target->GetName()
<< "\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVARegressionApplication is done!" << std::endl << std::endl;
--- Created root file: "TMVARegApp.root" containing the MVA output histograms ==> TMVARegressionApplication is done!