Trainer.hpp

/// @file Trainer.hpp
/// @author Franck Dary
/// @version 1.0
/// @date 2018-08-03

#ifndef TRAINER__H
#define TRAINER__H

#include "TransitionMachine.hpp"
#include "BD.hpp"
#include "Config.hpp"

/// @brief An object capable of training a TransitionMachine given a BD initialized with training examples.
class Trainer
{
  private :

  /// @brief The TransitionMachine that will be trained.
  TransitionMachine & tm;
  /// @brief The BD initialized with training examples.
  BD & trainBD;
  /// @brief The configuration of the TransitionMachine while processing trainBD.
  Config & trainConfig;

  /// @brief The BD initialized with dev examples.
  ///
  /// Can be nullptr if dev is not used in this training.
  BD * devBD;
  /// @brief The configuration of the TransitionMachine while processing devBD.
  ///
  /// Can be nullptr if dev is not used in this training.
  Config * devConfig;

  /// @brief For each classifier, a pair of number examples seen / number examples successfully classified
  std::map< std::string, std::pair<int, int> > trainCounter;

  /// @brief For each classifier, the train score for the current iteration.
  std::map< std::string, float > scores;

  /// @brief For each classifier, the best score seen on dev.
  std::map< std::string, float > bestScores;

  /// @brief Whether or not each Classifier topology has been printed.
  std::map< std::string, bool > topologyPrinted;

  /// @brief Current iteration.
  int curIter;

  public :

  /// @brief The FeatureDescritpion of a Config.
  using FD = FeatureModel::FeatureDescription;

  private :

  /// @brief Compute and print scores for each Classifier on this epoch, and save the Classifier if they achieved their all time best score.
  void printScoresAndSave(FILE * output);

  /// @brief Get the scores of the classifiers on the dev dataset.
  ///
  /// @return Map from each Classifier name to their score.
  std::map<std::string, float> getScoreOnDev();

  public :

  /// @brief Construct a new Trainer without a dev set.
  ///
  /// @param tm The TransitionMachine to use.
  /// @param bd The BD to use.
  /// @param config The config to use.
  Trainer(TransitionMachine & tm, BD & bd, Config & config);
  /// @brief Construct a new Trainer with a dev set.
  ///
  /// @param tm The TransitionMachine to use.
  /// @param bd The BD corresponding to the training dataset.
  /// @param config The Config corresponding to bd.
  /// @param devBD The BD corresponding to the dev dataset.
  /// @param devConfig The Config corresponding to devBD.
  Trainer(TransitionMachine & tm, BD & bd, Config & config, BD * devBD, Config * devConfig);

  /// @brief Train the TransitionMachine one example at a time.
  ///
  /// For each epoch all the Classifier of the TransitionMachine are fed all the 
  /// training examples, at the end of the epoch Classifier are evaluated on 
  /// the devBD if available, and each Classifier will be saved only if its score
  /// on the current epoch is its all time best.\n
  /// When a Classifier is saved that way, all the Dict involved are also saved.
  void train();
};

#endif