mirror of
https://github.com/moses-smt/mosesdecoder.git
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231 lines
5.6 KiB
C++
231 lines
5.6 KiB
C++
#ifndef MERT_SCORER_H_
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#define MERT_SCORER_H_
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#include <iostream>
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#include <sstream>
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#include <stdexcept>
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#include <string>
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#include <vector>
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#include "Types.h"
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#include "ScoreData.h"
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class PreProcessFilter;
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class ScoreStats;
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namespace mert {
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class Vocabulary;
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} // namespace mert
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enum ScorerRegularisationStrategy {REG_NONE, REG_AVERAGE, REG_MINIMUM};
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/**
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* Superclass of all scorers and dummy implementation.
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*
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* In order to add a new scorer it should be sufficient to override the members
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* prepareStats(), setReferenceFiles() and score() (or calculateScore()).
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*/
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class Scorer
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{
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public:
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Scorer(const std::string& name, const std::string& config);
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virtual ~Scorer();
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/**
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* Return the number of statistics needed for the computation of the score.
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*/
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virtual std::size_t NumberOfScores() const = 0;
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/**
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* Set the reference files. This must be called before prepareStats().
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*/
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virtual void setReferenceFiles(const std::vector<std::string>& referenceFiles) {
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// do nothing
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}
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/**
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* Process the given guessed text, corresponding to the given reference sindex
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* and add the appropriate statistics to the entry.
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*/
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virtual void prepareStats(std::size_t sindex, const std::string& text, ScoreStats& entry) {
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// do nothing.
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}
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virtual void prepareStats(const std::string& sindex, const std::string& text, ScoreStats& entry) {
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this->prepareStats(static_cast<std::size_t>(atoi(sindex.c_str())), text, entry);
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}
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/**
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* Score using each of the candidate index, then go through the diffs
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* applying each in turn, and calculating a new score each time.
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*/
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virtual void score(const candidates_t& candidates, const diffs_t& diffs,
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statscores_t& scores) const = 0;
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/*
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{
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//dummy impl
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if (!m_score_data) {
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throw runtime_error("score data not loaded");
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}
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scores.push_back(0);
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for (std::size_t i = 0; i < diffs.size(); ++i) {
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scores.push_back(0);
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}
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}
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*/
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/**
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* Calculate the score of the sentences corresponding to the list of candidate
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* indices. Each index indicates the 1-best choice from the n-best list.
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*/
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float score(const candidates_t& candidates) const {
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diffs_t diffs;
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statscores_t scores;
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score(candidates, diffs, scores);
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return scores[0];
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}
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const std::string& getName() const {
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return m_name;
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}
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std::size_t getReferenceSize() const {
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if (m_score_data) {
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return m_score_data->size();
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}
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return 0;
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}
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/**
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* Set the score data, prior to scoring.
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*/
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virtual void setScoreData(ScoreData* data) {
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m_score_data = data;
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}
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/**
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* Set the factors, which should be used for this metric
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*/
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virtual void setFactors(const std::string& factors);
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mert::Vocabulary* GetVocab() const { return m_vocab; }
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/**
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* Set unix filter, which will be used to preprocess the sentences
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*/
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virtual void setFilter(const std::string& filterCommand);
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private:
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void InitConfig(const std::string& config);
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/**
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* Take the factored sentence and return the desired factors
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*/
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std::string applyFactors(const std::string& sentece) const;
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/**
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* Preprocess the sentence with the filter (if given)
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*/
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std::string applyFilter(const std::string& sentence) const;
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std::string m_name;
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mert::Vocabulary* m_vocab;
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std::map<std::string, std::string> m_config;
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std::vector<int> m_factors;
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PreProcessFilter* m_filter;
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protected:
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ScoreData* m_score_data;
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bool m_enable_preserve_case;
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/**
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* Get value of config variable. If not provided, return default.
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*/
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std::string getConfig(const std::string& key, const std::string& def="") const {
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std::map<std::string,std::string>::const_iterator i = m_config.find(key);
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if (i == m_config.end()) {
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return def;
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} else {
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return i->second;
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}
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}
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/**
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* Tokenise line and encode.
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* Note: We assume that all tokens are separated by whitespaces.
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*/
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void TokenizeAndEncode(const std::string& line, std::vector<int>& encoded);
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/**
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* Every inherited scorer should call this function for each sentence
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*/
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std::string preprocessSentence(const std::string& sentence) const
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{
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return applyFactors(applyFilter(sentence));
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}
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};
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/**
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* Abstract base class for Scorers that work by adding statistics across all
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* outout sentences, then apply some formula, e.g., BLEU, PER.
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*/
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class StatisticsBasedScorer : public Scorer
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{
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public:
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StatisticsBasedScorer(const std::string& name, const std::string& config);
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virtual ~StatisticsBasedScorer() {}
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virtual void score(const candidates_t& candidates, const diffs_t& diffs,
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statscores_t& scores) const;
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protected:
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enum RegularisationType {
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NONE,
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AVERAGE,
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MINIMUM
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};
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/**
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* Calculate the actual score.
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*/
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virtual statscore_t calculateScore(const std::vector<int>& totals) const = 0;
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// regularisation
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RegularisationType m_regularization_type;
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std::size_t m_regularization_window;
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};
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namespace {
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//regularisation strategies
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inline float score_min(const statscores_t& scores, size_t start, size_t end)
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{
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float min = std::numeric_limits<float>::max();
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for (size_t i = start; i < end; ++i) {
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if (scores[i] < min) {
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min = scores[i];
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}
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}
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return min;
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}
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inline float score_average(const statscores_t& scores, size_t start, size_t end)
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{
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if ((end - start) < 1) {
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// this shouldn't happen
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return 0;
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}
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float total = 0;
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for (size_t j = start; j < end; ++j) {
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total += scores[j];
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}
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return total / (end - start);
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}
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} // namespace
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#endif // MERT_SCORER_H_
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