Python.h

/**************************************************************************
 * 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.                  *
 **************************************************************************/
 
#pragma once
 
#include <mva/interface/Options.h>
#include <mva/interface/Teacher.h>
#include <mva/interface/Expert.h>

#if !defined(__CLING__)
#include <Python.h>
#endif
 
#if !defined(__GNUG__) || defined(__ICC)
#else
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-local-typedefs"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#endif
#include <boost/python/object.hpp>
#include <boost/python/dict.hpp>
#include <boost/python/import.hpp>
#include <boost/python/extract.hpp>
#if !defined(__GNUG__) || defined(__ICC)
#else
#pragma GCC diagnostic pop
#endif
 
 
namespace Belle2 {
  namespace MVA {

    class PythonOptions : public SpecificOptions {
 
    public:
      virtual void load(const boost::property_tree::ptree& pt) override;

      virtual void save(boost::property_tree::ptree& pt) const override;

      virtual po::options_description getDescription() override;

      virtual std::string getMethod() const override { return "Python"; }
 
      std::string m_framework = "sklearn"; 
      std::string m_steering_file = ""; 
      std::string m_config = "null"; 
      unsigned int m_mini_batch_size = 0; 
      unsigned int m_nIterations = 1; 
      double m_training_fraction = 1.0; 
      bool m_normalize = false; 
    };
 

    class PythonTeacher : public Teacher {
 
    public:
      PythonTeacher(const GeneralOptions& general_options, const PythonOptions& specific_options);

      virtual Weightfile train(Dataset& training_data) const override;
 
    private:
      PythonOptions m_specific_options; 
    };

    class PythonExpert : public MVA::Expert {
 
    public:
      PythonExpert();

      virtual void load(Weightfile& weightfile) override;

      virtual std::vector<float> apply(Dataset& test_data) const override;

      virtual std::vector<std::vector<float>> applyMulticlass(Dataset& test_data) const override;
 
    protected:
      PythonOptions m_specific_options; 
      boost::python::object m_unique_mva_module; 
      boost::python::object m_state; 
      std::vector<float> m_means; 
      std::vector<float> m_stds; 
    };
 
  }
}