CbmKresTrainAnn.cxx

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/* Copyright (C) 2017-2020 GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt
   SPDX-License-Identifier: GPL-3.0-only
   Authors: Ievgenii Kres, Florian Uhlig [committer] */
 
#include "CbmKresTrainAnn.h"
 
#include "CbmDrawHist.h"
 
#include "TCanvas.h"
#include "TH1D.h"
#include "TH2D.h"
#include "TMath.h"
#include "TMultiLayerPerceptron.h"
#include "TSystem.h"
#include "TTree.h"
 
#include <boost/assign/list_of.hpp>
 
#include <iostream>
#include <string>
#include <vector>
 
#include <cmath>
 
 
using boost::assign::list_of;
using namespace std;
 
CbmKresTrainAnn::CbmKresTrainAnn()
  : fMaxNofTrainSamples(1500)
  , fAnnCut(0.6)
  , fNofWrongLikeCorrect(0)
  , fNofCorrectLikeWrong(0)
  , IM_correct()
  , OA_correct()
  , Angle_correct()
  , Z_correct()
  , Mom1_correct()
  , Mom2_correct()
  , IM_wrong()
  , OA_wrong()
  , Angle_wrong()
  , Z_wrong()
  , Mom1_wrong()
  , Mom2_wrong()
  , fHists()
  , fhAnnOutput_correct(nullptr)
  , fhAnnOutput_wrong(nullptr)
  , fhCumProb_correct(nullptr)
  , fhCumProb_wrong(nullptr)
{
}
 
CbmKresTrainAnn::~CbmKresTrainAnn() {}
 
void CbmKresTrainAnn::Init() { InitHistograms(); }
 
void CbmKresTrainAnn::Exec(int event, int IdForANN, double InvariantMass, double OpeningAngle, double PlaneAngle_last,
                           double ZPos, TVector3 Momentum1, TVector3 Momentum2)
{
  double p1 =
    TMath::Sqrt(Momentum1.X() * Momentum1.X() + Momentum1.Y() * Momentum1.Y() + Momentum1.Z() * Momentum1.Z());
  double p2 =
    TMath::Sqrt(Momentum2.X() * Momentum2.X() + Momentum2.Y() * Momentum2.Y() + Momentum2.Z() * Momentum2.Z());
  if (IdForANN == 1) {
    //if (IM_correct.size() < fMaxNofTrainSamples){
    IM_correct.push_back(InvariantMass);
    OA_correct.push_back(OpeningAngle);
    Angle_correct.push_back(PlaneAngle_last);
    Z_correct.push_back(ZPos);
    Mom1_correct.push_back(p1);
    Mom2_correct.push_back(p2);
    //}
  }
  else {
    //if (IM_wrong.size() < fMaxNofTrainSamples){
    IM_wrong.push_back(InvariantMass);
    OA_wrong.push_back(OpeningAngle);
    Angle_wrong.push_back(PlaneAngle_last);
    Z_wrong.push_back(ZPos);
    Mom1_wrong.push_back(p1);
    Mom2_wrong.push_back(p2);
    //}
  }
 
  if (IM_correct.size() % 100 == 0 && IdForANN == 1)
    cout << "correct = " << IM_correct.size() << ";  wrong = " << IM_wrong.size() << endl;
 
  //if (IM_correct.size() >= fMaxNofTrainSamples && IM_wrong.size() >= fMaxNofTrainSamples) {
  if (event == 2000 && IM_correct.size() >= fMaxNofTrainSamples) {
    TrainAndTestAnn();
    Draw();
 
    IM_correct.clear();
    OA_correct.clear();
    Angle_correct.clear();
    Z_correct.clear();
    Mom1_correct.clear();
    Mom2_correct.clear();
    IM_wrong.clear();
    OA_wrong.clear();
    Angle_wrong.clear();
    Z_wrong.clear();
    Mom1_wrong.clear();
    Mom2_wrong.clear();
  }
}
 
void CbmKresTrainAnn::TrainAndTestAnn()
{
  //cout << "Do TrainAndTestAnn" << endl;
  TTree* simu = new TTree("MonteCarlo", "MontecarloData");
  Double_t x[6];
  Double_t xOut;
 
  simu->Branch("x0", &x[0], "x0/D");
  simu->Branch("x1", &x[1], "x1/D");
  simu->Branch("x2", &x[2], "x2/D");
  simu->Branch("x3", &x[3], "x3/D");
  simu->Branch("x4", &x[4], "x4/D");
  simu->Branch("x5", &x[5], "x5/D");
  simu->Branch("xOut", &xOut, "xOut/D");
 
  for (size_t i = 0; i < IM_correct.size(); i++) {
    x[0] = IM_correct[i] / 0.1;
    x[1] = OA_correct[i] / 30;
    x[2] = Angle_correct[i] / 30;
    x[3] = Z_correct[i] / 100;
    x[4] = Mom1_correct[i] / 5;
    x[5] = Mom2_correct[i] / 5;
 
    if (x[0] > 1.0) x[0] = 1.0;
    if (x[1] > 1.0) x[1] = 1.0;
    if (x[2] > 1.0) x[2] = 1.0;
    if (x[3] > 1.0) x[3] = 1.0;
    if (x[4] > 1.0) x[4] = 1.0;
    if (x[5] > 1.0) x[5] = 1.0;
 
    xOut = 1.;
    simu->Fill();
    if (i >= fMaxNofTrainSamples) break;
  }
  for (size_t i = 0; i < IM_wrong.size(); i++) {
    x[0] = IM_wrong[i] / 0.1;
    x[1] = OA_wrong[i] / 30;
    x[2] = Angle_wrong[i] / 30;
    x[3] = Z_wrong[i] / 100;
    x[4] = Mom1_wrong[i] / 5;
    x[5] = Mom2_wrong[i] / 5;
 
    if (x[0] > 1.0) x[0] = 1.0;
    if (x[1] > 1.0) x[1] = 1.0;
    if (x[2] > 1.0) x[2] = 1.0;
    if (x[3] > 1.0) x[3] = 1.0;
    if (x[4] > 1.0) x[4] = 1.0;
    if (x[5] > 1.0) x[5] = 1.0;
 
    xOut = -1.;
    simu->Fill();
    if (i >= fMaxNofTrainSamples) break;
  }
 
  TMultiLayerPerceptron network("x0,x1,x2,x3,x4,x5:12:xOut", simu, "Entry$+1");
  network.Train(300, "text,update=10");
  network.DumpWeights("../../../analysis/conversion2/KresAnalysis_ann_weights.txt");
 
 
  Double_t params[6];
  fNofWrongLikeCorrect = 0;
  fNofCorrectLikeWrong = 0;
 
  for (size_t i = 0; i < IM_correct.size(); i++) {
    params[0] = IM_correct[i] / 0.1;
    params[1] = OA_correct[i] / 30;
    params[2] = Angle_correct[i] / 30;
    params[3] = Z_correct[i] / 100;
    params[4] = Mom1_correct[i] / 5;
    params[5] = Mom2_correct[i] / 5;
 
    if (params[0] > 1.0) params[0] = 1.0;
    if (params[1] > 1.0) params[1] = 1.0;
    if (params[2] > 1.0) params[2] = 1.0;
    if (params[3] > 1.0) params[3] = 1.0;
    if (params[4] > 1.0) params[4] = 1.0;
    if (params[5] > 1.0) params[5] = 1.0;
 
    Double_t netEval = network.Evaluate(0, params);
    fhAnnOutput_correct->Fill(netEval);
    if (netEval < fAnnCut) fNofCorrectLikeWrong++;
  }
  for (size_t i = 0; i < IM_wrong.size(); i++) {
    params[0] = IM_wrong[i] / 0.1;
    params[1] = OA_wrong[i] / 30;
    params[2] = Angle_wrong[i] / 30;
    params[3] = Z_wrong[i] / 100;
    params[4] = Mom1_wrong[i] / 5;
    params[5] = Mom2_wrong[i] / 5;
 
    if (params[0] > 1.0) params[0] = 1.0;
    if (params[1] > 1.0) params[1] = 1.0;
    if (params[2] > 1.0) params[2] = 1.0;
    if (params[3] > 1.0) params[3] = 1.0;
    if (params[4] > 1.0) params[4] = 1.0;
    if (params[5] > 1.0) params[5] = 1.0;
 
    Double_t netEval = network.Evaluate(0, params);
    fhAnnOutput_wrong->Fill(netEval);
    if (netEval >= fAnnCut) fNofWrongLikeCorrect++;
  }
}
 
 
void CbmKresTrainAnn::Draw()
{
  cout << "nof correct pairs = " << IM_correct.size() << endl;
  cout << "nof wrong pairs = " << IM_wrong.size() << endl;
  cout << "wrong like correct = " << fNofWrongLikeCorrect
       << ", wrong supp = " << (Double_t) IM_wrong.size() / fNofWrongLikeCorrect << endl;
  cout << "Correct like wrong = " << fNofCorrectLikeWrong
       << ", correct lost eff = " << 100. * (Double_t) fNofCorrectLikeWrong / IM_correct.size() << endl;
 
  Double_t cumProbFake = 0.;
  Double_t cumProbTrue = 0.;
  Int_t nofTrue        = (Int_t) fhAnnOutput_correct->GetEntries();
  Int_t nofFake        = (Int_t) fhAnnOutput_wrong->GetEntries();
 
  for (Int_t i = 1; i <= fhAnnOutput_wrong->GetNbinsX(); i++) {
    cumProbTrue += fhAnnOutput_correct->GetBinContent(i);
    fhCumProb_correct->SetBinContent(i, 1. - (Double_t) cumProbTrue / nofTrue);
 
    cumProbFake += fhAnnOutput_wrong->GetBinContent(i);
    fhCumProb_wrong->SetBinContent(i, (Double_t) cumProbFake / nofFake);
  }
 
 
  TCanvas* c1 = new TCanvas("ann_correct_ann_output", "ann_correct_ann_output", 400, 400);
  c1->SetTitle("ann_correct_ann_output");
  fhAnnOutput_correct->Draw();
 
  TCanvas* c2 = new TCanvas("ann_wrong_ann_output", "ann_wrong_ann_output", 400, 400);
  c2->SetTitle("ann_wrong_ann_output");
  fhAnnOutput_wrong->Draw();
 
  TCanvas* c3 = new TCanvas("ann_correct_cum_prob", "ann_correct_cum_prob", 400, 400);
  c3->SetTitle("ann_correct_cum_prob");
  fhCumProb_correct->Draw();
 
  TCanvas* c4 = new TCanvas("ann_wrong_cum_prob", "ann_wrong_cum_prob", 400, 400);
  c4->SetTitle("ann_wrong_cum_prob");
  fhCumProb_wrong->Draw();
}
 
 
void CbmKresTrainAnn::InitHistograms()
{
 
  fhAnnOutput_correct = new TH1D("fhAnnOutput_correct", "ANN output;ANN output;Counter", 100, -1.2, 1.2);
  fHists.push_back(fhAnnOutput_correct);
  fhAnnOutput_wrong = new TH1D("fhAnnOutput_wrong", "ANN output;ANN output;Counter", 100, -1.2, 1.2);
  fHists.push_back(fhAnnOutput_wrong);
 
  fhCumProb_correct = new TH1D("fhCumProb_correct", "ANN output;ANN output;Cumulative probability", 100, -1.2, 1.2);
  fHists.push_back(fhCumProb_correct);
  fhCumProb_wrong = new TH1D("fhCumProb_wrong", "ANN output;ANN output;Cumulative probability", 100, -1.2, 1.2);
  fHists.push_back(fhCumProb_wrong);
}