NNFitterTest.cc

/**************************************************************************
 * basf2 (Belle II Analysis Software Framework)                           *
 * Author: The Belle II Collaboration                                     *
 *                                                                        *
 * See git log for contributors and copyright holders.                    *
 * This file is licensed under LGPL-3.0, see LICENSE.md.                  *
 **************************************************************************/
 
#include <svd/simulation/SVDSimulationTools.h>
#include <svd/reconstruction/NNWaveFitter.h>
#include <svd/reconstruction/NNWaveFitTool.h>
#include <gtest/gtest.h>
#include <iostream>
#include <fstream>
#include <sstream>
#include <string>
#include <tuple>
 
using namespace std;
 
namespace Belle2 {
  namespace SVD {
 
    TEST(NNTimeFitter, DISABLED_CompareNetworkCoefficient)
    {
      // Create an instance of the NN fitter
      NNWaveFitter fitter("svd/data/SVDTimeNet.xml");
      EXPECT_TRUE(fitter.checkCoefficients("svd/data/classifier.txt", 1.0e-6));
    }
 
    TEST(NNTimeFitter, DISABLED_CompareFits)
    {
      const size_t max_lines = 100; // maximum number of lines to be read
 
      // Create an instance of the NN fitter and the fitter tool.
      NNWaveFitter fitter("SVDTimeNet_6samples");
      auto fitTool = fitter.getFitTool();
      size_t nProbs = fitTool.getBinCenters().size();
 
      ifstream infile("svd/data/test_sample.csv");
 
      // Read the rows one by one and compare results
      string line;
      getline(infile, line);
 
      for (size_t i_line = 0; i_line < max_lines; i_line++) {
 
        getline(infile, line);
        if (line.size() < 10) break;
        istringstream sline(line);
 
        // Parse header. We want the dimennsion of the probability array.
        // not needed
        string cell;
        getline(sline, cell, ','); // index
        getline(sline, cell, ','); // test
 
        // true values. Read from the file, though not used.
        getline(sline, cell, ',');
        [[maybe_unused]] double true_amp = stod(cell);
        getline(sline, cell, ',');
        [[maybe_unused]] double true_t0 = stod(cell);
        getline(sline, cell, ',');
        [[maybe_unused]] double width = stod(cell);
        getline(sline, cell, ',');
        [[maybe_unused]] double noise = stod(cell);
 
        // normalized samples
        apvSamples normedSamples;
        for (size_t iSample = 0; iSample < nAPVSamples; ++iSample) {
          getline(sline, cell, ',');
          normedSamples[iSample] = stod(cell);
        }
 
        // not needed
        getline(sline, cell, ',');
        getline(sline, cell, ',');
        getline(sline, cell, ',');
 
        // probabilities
        nnFitterBinData ProbsPy(nProbs);
        for (size_t iSample = 0; iSample < nProbs; ++iSample) {
          getline(sline, cell, ',');
          ProbsPy[iSample] = stod(cell);
        }
 
        // fit results
        getline(sline, cell, ',');
        double fitPy_amp = stod(cell);
        getline(sline, cell, ',');
        double fitPy_ampSigma = stod(cell);
        getline(sline, cell, ',');
        [[maybe_unused]] double fitPy_chi2 = stod(cell);
        getline(sline, cell, ',');
        double fitPy_t0 = stod(cell);
        getline(sline, cell, ',');
        double fitPy_t0Sigma = stod(cell);
 
        // now do the Cpp fit
        const shared_ptr<nnFitterBinData> ProbsCpp = fitter.getFit(normedSamples, width);
        for (size_t iBin = 0; iBin < nProbs; ++iBin)
          EXPECT_NEAR((*ProbsCpp)[iBin], ProbsPy[iBin], 5.0e-3);
 
        double t0_cpp, t0_err_cpp;
        tie(t0_cpp, t0_err_cpp) = fitTool.getTimeShift(*ProbsCpp);
        EXPECT_NEAR(t0_cpp, fitPy_t0, 5);
        EXPECT_NEAR(t0_err_cpp, fitPy_t0Sigma, 2);
 
        double amp_cpp, amp_err_cpp, chi2_cpp;
        tie(amp_cpp, amp_err_cpp, chi2_cpp) = fitTool.getAmplitudeChi2(normedSamples, t0_cpp, width);
        EXPECT_NEAR(amp_cpp, fitPy_amp, 1.0);
        EXPECT_NEAR(amp_err_cpp, fitPy_ampSigma, 0.1);
        // FIXME: This is calculated slightly differently in Python, and it shows.
        // EXPECT_NEAR(chi2_cpp, fitPy_chi2, 1);
      }
    }
 
  } // namespace SVD
}  // namespace Belle2