mira-style-model.H

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#ifndef RERANKER_MIRA_STYLE_MODEL_H_
#define RERANKER_MIRA_STYLE_MODEL_H_

#include <cstdlib>
#include <string>

#include "kernel-function.H"
#include "symbol-table.H"

#include "perceptron-model.H"

#define DEFAULT_MIRA_CLIP 0.1

namespace reranker {

using std::string;

class DirectLossScoreComparator : public Candidate::Comparator {
 public:
  virtual int Compare(const Model &model,
                      const Candidate &c1, const Candidate &c2) {
    double score_diff = (c1.score() + c1.loss()) - (c2.score() + c2.loss());
    return score_diff == 0.0 ? 0 : (score_diff < 0.0 ? -1 : 1);
  }
};

class MiraStyleModel : public PerceptronModel {
 public:
  MiraStyleModel() : PerceptronModel(), mira_clip_(DEFAULT_MIRA_CLIP) { }
  MiraStyleModel(const string &name) : PerceptronModel(name),
                                       mira_clip_(DEFAULT_MIRA_CLIP) { }
  MiraStyleModel(const string &name, KernelFunction *kernel_fn) :
      PerceptronModel(name, kernel_fn),
      mira_clip_(DEFAULT_MIRA_CLIP) { }
  MiraStyleModel(const string &name, KernelFunction *kernel_fn,
                 Symbols *symbols) :
      PerceptronModel(name, kernel_fn, symbols),
      mira_clip_(DEFAULT_MIRA_CLIP) { }


  virtual void RegisterInitializers(Initializers &initializers) {
    PerceptronModel::RegisterInitializers(initializers);
    initializers.Add("mira_clip", &mira_clip_);
  }

  void set_mira_clip(double mira_clip) { mira_clip_ = mira_clip; }

  virtual double ComputeStepSize(
      const unordered_set<int> &gold_features,
      const unordered_set<int> &best_scoring_features,
      const CandidateSet &example) {
    FeatureVector<int,double> vector_diff;
    vector_diff.AddScaledSubvector(gold_features,
                                   example.GetGold().features(), 1.0);
    vector_diff.AddScaledSubvector(best_scoring_features,
                                   example.GetBestScoring().features(), -1.0);
    double loss_weight = use_weighted_loss() ? example.loss_weight() : 1.0;
    double loss_diff =
        loss_weight *
        (example.GetBestScoring().loss() - example.GetGold().loss());
    double score_diff =
        example.GetBestScoring().score() - example.GetGold().score();
    double raw_step = (loss_diff + score_diff) / vector_diff.Dot(vector_diff);
    step_size_ = raw_step > mira_clip_ ? mira_clip_ : raw_step;
    return step_size_;
  }
 private:
  // data members
  double mira_clip_;
};

}  // namespace reranker

#endif