test_Trivial.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 <mva/methods/Trivial.h>
#include <mva/interface/Interface.h>
#include <framework/utilities/FileSystem.h>
#include <framework/utilities/TestHelpers.h>
 
#include <gtest/gtest.h>
 
using namespace Belle2;
 
namespace {
 
  TEST(TrivialTest, TrivialOptions)
  {
 
    MVA::TrivialOptions specific_options;
 
    EXPECT_EQ(specific_options.m_output, 0.5);
 
    specific_options.m_output = 0.1;
 
    boost::property_tree::ptree pt;
    specific_options.save(pt);
    EXPECT_FLOAT_EQ(pt.get<double>("Trivial_output"), 0.1);
 
    MVA::TrivialOptions specific_options2;
    specific_options2.load(pt);
 
    EXPECT_EQ(specific_options2.m_output, 0.1);
 
    EXPECT_EQ(specific_options.getMethod(), std::string("Trivial"));
 
    // Test if po::options_description is created without crashing
    auto description = specific_options.getDescription();
    EXPECT_EQ(description.options().size(), 1);
 
    // Check for B2ERROR and throw if version is wrong
    // we try with version 100, surely we will never reach this!
    pt.put("Trivial_version", 100);
    try {
      EXPECT_B2ERROR(specific_options2.load(pt));
    } catch (...) {
 
    }
    EXPECT_THROW(specific_options2.load(pt), std::runtime_error);
  }
 
  class TestDataset : public MVA::Dataset {
  public:
    explicit TestDataset(const std::vector<float>& data) : MVA::Dataset(MVA::GeneralOptions()), m_data(data)
    {
      m_input = {0.0};
      m_target = 0.0;
      m_isSignal = false;
      m_weight = 1.0;
    }
 
    [[nodiscard]] unsigned int getNumberOfFeatures() const override { return 1; }
    [[nodiscard]] unsigned int getNumberOfSpectators() const override { return 0; }
    [[nodiscard]] unsigned int getNumberOfEvents() const override { return m_data.size(); }
    void loadEvent(unsigned int iEvent) override { m_input[0] = m_data[iEvent]; m_target = iEvent % 2; m_isSignal = m_target == 1; };
    float getSignalFraction() override { return 0.1; };
    std::vector<float> getFeature(unsigned int) override { return m_data; }
 
    std::vector<float> m_data;
 
  };
 
 
  TEST(TrivialTest, TrivialInterface)
  {
    MVA::Interface<MVA::TrivialOptions, MVA::TrivialTeacher, MVA::TrivialExpert> interface;
 
    MVA::GeneralOptions general_options;
    MVA::TrivialOptions specific_options;
    TestDataset dataset({1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 2.0, 3.0});
 
    auto teacher = interface.getTeacher(general_options, specific_options);
    auto weightfile = teacher->train(dataset);
 
    auto expert = interface.getExpert();
    expert->load(weightfile);
    auto probabilities = expert->apply(dataset);
    EXPECT_EQ(probabilities.size(), dataset.getNumberOfEvents());
    for (unsigned int i = 0; i < dataset.getNumberOfEvents(); ++i)
      EXPECT_FLOAT_EQ(probabilities[i], 0.5);
 
  }
 
}