swh:1:snp:af87cd67498ef4fe47c76ed3e7caffe5b61facaf
Tip revision: ae2cc3736f6bfd963f99d30bb39dfd8b1fd0699e authored by Fons Rademakers on 14 March 2011, 14:14:39 UTC
tag patch release v5-28-00b.
tag patch release v5-28-00b.
Tip revision: ae2cc37
TMVAClassificationApplication.C
/**********************************************************************************
* Project : TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package : TMVA *
* Exectuable: TMVAClassificationApplication *
* *
* This macro provides a simple example on how to use the trained classifiers *
* within an analysis module *
**********************************************************************************/
#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 "TMVAGui.C"
#if not defined(__CINT__) || defined(__MAKECINT__)
#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
#include "TMVA/MethodCuts.h"
#endif
using namespace TMVA;
void TMVAClassificationApplication( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 1;
Use["CutsD"] = 1;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 1;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 1;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 1; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 1; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 1; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 1;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 1;
// ---------------------------------------------------------------
Use["Plugin"] = 0;
Use["Category"] = 0;
Use["SVM_Gauss"] = 0;
Use["SVM_Poly"] = 0;
Use["SVM_Lin"] = 0;
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
// 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
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t var1, var2;
Float_t var3, var4;
reader->AddVariable( "myvar1 := var1+var2", &var1 );
reader->AddVariable( "myvar2 := var1-var2", &var2 );
reader->AddVariable( "var3", &var3 );
reader->AddVariable( "var4", &var4 );
// 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 );
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// Add artificial spectators for distinguishing categories
reader->AddSpectator( "Category_cat1 := var3<=0", &Category_cat1 );
reader->AddSpectator( "Category_cat2 := (var3>0)&&(var4<0)", &Category_cat2 );
reader->AddSpectator( "Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
}
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAClassification";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 100;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0);
TH1F *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0), *histRf(0), *histSVMG(0);
TH1F *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
if (Use["Likelihood"]) histLk = new TH1F( "MVA_Likelihood", "MVA_Likelihood", nbin, -1, 1 );
if (Use["LikelihoodD"]) histLkD = new TH1F( "MVA_LikelihoodD", "MVA_LikelihoodD", nbin, -1, 0.9999 );
if (Use["LikelihoodPCA"]) histLkPCA = new TH1F( "MVA_LikelihoodPCA", "MVA_LikelihoodPCA", nbin, -1, 1 );
if (Use["LikelihoodKDE"]) histLkKDE = new TH1F( "MVA_LikelihoodKDE", "MVA_LikelihoodKDE", nbin, -0.00001, 0.99999 );
if (Use["LikelihoodMIX"]) histLkMIX = new TH1F( "MVA_LikelihoodMIX", "MVA_LikelihoodMIX", nbin, 0, 1 );
if (Use["PDERS"]) histPD = new TH1F( "MVA_PDERS", "MVA_PDERS", nbin, 0, 1 );
if (Use["PDERSD"]) histPDD = new TH1F( "MVA_PDERSD", "MVA_PDERSD", nbin, 0, 1 );
if (Use["PDERSPCA"]) histPDPCA = new TH1F( "MVA_PDERSPCA", "MVA_PDERSPCA", nbin, 0, 1 );
if (Use["KNN"]) histKNN = new TH1F( "MVA_KNN", "MVA_KNN", nbin, 0, 1 );
if (Use["HMatrix"]) histHm = new TH1F( "MVA_HMatrix", "MVA_HMatrix", nbin, -0.95, 1.55 );
if (Use["Fisher"]) histFi = new TH1F( "MVA_Fisher", "MVA_Fisher", nbin, -4, 4 );
if (Use["FisherG"]) histFiG = new TH1F( "MVA_FisherG", "MVA_FisherG", nbin, -1, 1 );
if (Use["BoostedFisher"]) histFiB = new TH1F( "MVA_BoostedFisher", "MVA_BoostedFisher", nbin, -2, 2 );
if (Use["LD"]) histLD = new TH1F( "MVA_LD", "MVA_LD", nbin, -2, 2 );
if (Use["MLP"]) histNn = new TH1F( "MVA_MLP", "MVA_MLP", nbin, -1.25, 1.5 );
if (Use["MLPBFGS"]) histNnbfgs = new TH1F( "MVA_MLPBFGS", "MVA_MLPBFGS", nbin, -1.25, 1.5 );
if (Use["MLPBNN"]) histNnbnn = new TH1F( "MVA_MLPBNN", "MVA_MLPBNN", nbin, -1.25, 1.5 );
if (Use["CFMlpANN"]) histNnC = new TH1F( "MVA_CFMlpANN", "MVA_CFMlpANN", nbin, 0, 1 );
if (Use["TMlpANN"]) histNnT = new TH1F( "MVA_TMlpANN", "MVA_TMlpANN", nbin, -1.3, 1.3 );
if (Use["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", nbin, -1.0, 1.0 );
if (Use["RuleFit"]) histRf = new TH1F( "MVA_RuleFit", "MVA_RuleFit", nbin, -2.0, 2.0 );
if (Use["SVM_Gauss"]) histSVMG = new TH1F( "MVA_SVM_Gauss", "MVA_SVM_Gauss", nbin, 0.0, 1.0 );
if (Use["SVM_Poly"]) histSVMP = new TH1F( "MVA_SVM_Poly", "MVA_SVM_Poly", nbin, 0.0, 1.0 );
if (Use["SVM_Lin"]) histSVML = new TH1F( "MVA_SVM_Lin", "MVA_SVM_Lin", nbin, 0.0, 1.0 );
if (Use["FDA_MT"]) histFDAMT = new TH1F( "MVA_FDA_MT", "MVA_FDA_MT", nbin, -2.0, 3.0 );
if (Use["FDA_GA"]) histFDAGA = new TH1F( "MVA_FDA_GA", "MVA_FDA_GA", nbin, -2.0, 3.0 );
if (Use["Category"]) histCat = new TH1F( "MVA_Category", "MVA_Category", nbin, -2., 2. );
if (Use["Plugin"]) histPBdt = new TH1F( "MVA_PBDT", "MVA_BDT", nbin, -0.8, 0.8 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
if (Use["PDEFoam"]) {
histPDEFoam = new TH1F( "MVA_PDEFoam", "MVA_PDEFoam", nbin, 0, 1 );
histPDEFoamErr = new TH1F( "MVA_PDEFoamErr", "MVA_PDEFoam error", nbin, 0, 1 );
histPDEFoamSig = new TH1F( "MVA_PDEFoamSig", "MVA_PDEFoam significance", nbin, 0, 10 );
}
// Book example histogram for probability (the other methods are done similarly)
TH1F *probHistFi(0), *rarityHistFi(0);
if (Use["Fisher"]) {
probHistFi = new TH1F( "MVA_Fisher_Proba", "MVA_Fisher_Proba", nbin, 0, 1 );
rarityHistFi = new TH1F( "MVA_Fisher_Rarity", "MVA_Fisher_Rarity", nbin, 0, 1 );
}
// 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_example.root";
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("TreeS");
Float_t userVar1, userVar2;
theTree->SetBranchAddress( "var1", &userVar1 );
theTree->SetBranchAddress( "var2", &userVar2 );
theTree->SetBranchAddress( "var3", &var3 );
theTree->SetBranchAddress( "var4", &var4 );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
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);
var1 = userVar1 + userVar2;
var2 = userVar1 - userVar2;
// --- Return the MVA outputs and fill into histograms
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
if (Use["Likelihood" ]) histLk ->Fill( reader->EvaluateMVA( "Likelihood method" ) );
if (Use["LikelihoodD" ]) histLkD ->Fill( reader->EvaluateMVA( "LikelihoodD method" ) );
if (Use["LikelihoodPCA"]) histLkPCA ->Fill( reader->EvaluateMVA( "LikelihoodPCA method" ) );
if (Use["LikelihoodKDE"]) histLkKDE ->Fill( reader->EvaluateMVA( "LikelihoodKDE method" ) );
if (Use["LikelihoodMIX"]) histLkMIX ->Fill( reader->EvaluateMVA( "LikelihoodMIX method" ) );
if (Use["PDERS" ]) histPD ->Fill( reader->EvaluateMVA( "PDERS method" ) );
if (Use["PDERSD" ]) histPDD ->Fill( reader->EvaluateMVA( "PDERSD method" ) );
if (Use["PDERSPCA" ]) histPDPCA ->Fill( reader->EvaluateMVA( "PDERSPCA method" ) );
if (Use["KNN" ]) histKNN ->Fill( reader->EvaluateMVA( "KNN method" ) );
if (Use["HMatrix" ]) histHm ->Fill( reader->EvaluateMVA( "HMatrix method" ) );
if (Use["Fisher" ]) histFi ->Fill( reader->EvaluateMVA( "Fisher method" ) );
if (Use["FisherG" ]) histFiG ->Fill( reader->EvaluateMVA( "FisherG method" ) );
if (Use["BoostedFisher"]) histFiB ->Fill( reader->EvaluateMVA( "BoostedFisher method" ) );
if (Use["LD" ]) histLD ->Fill( reader->EvaluateMVA( "LD method" ) );
if (Use["MLP" ]) histNn ->Fill( reader->EvaluateMVA( "MLP method" ) );
if (Use["MLPBFGS" ]) histNnbfgs ->Fill( reader->EvaluateMVA( "MLPBFGS method" ) );
if (Use["MLPBNN" ]) histNnbnn ->Fill( reader->EvaluateMVA( "MLPBNN method" ) );
if (Use["CFMlpANN" ]) histNnC ->Fill( reader->EvaluateMVA( "CFMlpANN method" ) );
if (Use["TMlpANN" ]) histNnT ->Fill( reader->EvaluateMVA( "TMlpANN method" ) );
if (Use["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG method" ) );
if (Use["RuleFit" ]) histRf ->Fill( reader->EvaluateMVA( "RuleFit method" ) );
if (Use["SVM_Gauss" ]) histSVMG ->Fill( reader->EvaluateMVA( "SVM_Gauss method" ) );
if (Use["SVM_Poly" ]) histSVMP ->Fill( reader->EvaluateMVA( "SVM_Poly method" ) );
if (Use["SVM_Lin" ]) histSVML ->Fill( reader->EvaluateMVA( "SVM_Lin method" ) );
if (Use["FDA_MT" ]) histFDAMT ->Fill( reader->EvaluateMVA( "FDA_MT method" ) );
if (Use["FDA_GA" ]) histFDAGA ->Fill( reader->EvaluateMVA( "FDA_GA method" ) );
if (Use["Category" ]) histCat ->Fill( reader->EvaluateMVA( "Category method" ) );
if (Use["Plugin" ]) histPBdt ->Fill( reader->EvaluateMVA( "P_BDT method" ) );
// Retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err );
}
// Retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
if (Use["CutsGA"]) std::cout << "--- Efficiency for CutsGA method: " << double(nSelCutsGA)/theTree->GetEntries()
<< " (for a required signal efficiency of " << effS << ")" << std::endl;
if (Use["CutsGA"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
if (mcuts) {
std::vector<Double_t> cutsMin;
std::vector<Double_t> cutsMax;
mcuts->GetCuts( 0.7, cutsMin, cutsMax );
std::cout << "--- -------------------------------------------------------------" << std::endl;
std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
for (UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
std::cout << "... Cut: "
<< cutsMin[ivar]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
TFile *target = new TFile( "TMVApp.root","RECREATE" );
if (Use["Likelihood" ]) histLk ->Write();
if (Use["LikelihoodD" ]) histLkD ->Write();
if (Use["LikelihoodPCA"]) histLkPCA ->Write();
if (Use["LikelihoodKDE"]) histLkKDE ->Write();
if (Use["LikelihoodMIX"]) histLkMIX ->Write();
if (Use["PDERS" ]) histPD ->Write();
if (Use["PDERSD" ]) histPDD ->Write();
if (Use["PDERSPCA" ]) histPDPCA ->Write();
if (Use["KNN" ]) histKNN ->Write();
if (Use["HMatrix" ]) histHm ->Write();
if (Use["Fisher" ]) histFi ->Write();
if (Use["FisherG" ]) histFiG ->Write();
if (Use["BoostedFisher"]) histFiB ->Write();
if (Use["LD" ]) histLD ->Write();
if (Use["MLP" ]) histNn ->Write();
if (Use["MLPBFGS" ]) histNnbfgs ->Write();
if (Use["MLPBNN" ]) histNnbnn ->Write();
if (Use["CFMlpANN" ]) histNnC ->Write();
if (Use["TMlpANN" ]) histNnT ->Write();
if (Use["BDT" ]) histBdt ->Write();
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTG" ]) histBdtG ->Write();
if (Use["RuleFit" ]) histRf ->Write();
if (Use["SVM_Gauss" ]) histSVMG ->Write();
if (Use["SVM_Poly" ]) histSVMP ->Write();
if (Use["SVM_Lin" ]) histSVML ->Write();
if (Use["FDA_MT" ]) histFDAMT ->Write();
if (Use["FDA_GA" ]) histFDAGA ->Write();
if (Use["Category" ]) histCat ->Write();
if (Use["Plugin" ]) histPBdt ->Write();
// Write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// Write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
target->Close();
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
