mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-27 22:14:57 +03:00
359 lines
9.8 KiB
C++
359 lines
9.8 KiB
C++
#include "BleuScorer.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <climits>
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#include <fstream>
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#include <iostream>
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#include <stdexcept>
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#include "util/check.hh"
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#include "Ngram.h"
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#include "Reference.h"
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#include "Util.h"
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#include "ScoreDataIterator.h"
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#include "FeatureDataIterator.h"
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#include "Vocabulary.h"
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using namespace std;
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namespace
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{
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// configure regularisation
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const char KEY_REFLEN[] = "reflen";
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const char REFLEN_AVERAGE[] = "average";
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const char REFLEN_SHORTEST[] = "shortest";
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const char REFLEN_CLOSEST[] = "closest";
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} // namespace
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namespace MosesTuning
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{
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BleuScorer::BleuScorer(const string& config)
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: StatisticsBasedScorer("BLEU", config),
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m_ref_length_type(CLOSEST)
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{
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const string reflen = getConfig(KEY_REFLEN, REFLEN_CLOSEST);
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if (reflen == REFLEN_AVERAGE) {
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m_ref_length_type = AVERAGE;
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} else if (reflen == REFLEN_SHORTEST) {
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m_ref_length_type = SHORTEST;
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} else if (reflen == REFLEN_CLOSEST) {
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m_ref_length_type = CLOSEST;
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} else {
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throw runtime_error("Unknown reference length strategy: " + reflen);
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}
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}
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BleuScorer::~BleuScorer() {}
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size_t BleuScorer::CountNgrams(const string& line, NgramCounts& counts,
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unsigned int n, bool is_testing)
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{
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assert(n > 0);
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vector<int> encoded_tokens;
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// When performing tokenization of a hypothesis translation, we don't have
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// to update the Scorer's word vocabulary. However, the tokenization of
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// reference translations requires modifying the vocabulary, which means
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// this procedure might be slower than the tokenization the hypothesis
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// translation.
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if (is_testing) {
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TokenizeAndEncodeTesting(line, encoded_tokens);
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} else {
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TokenizeAndEncode(line, encoded_tokens);
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}
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const size_t len = encoded_tokens.size();
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vector<int> ngram;
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for (size_t k = 1; k <= n; ++k) {
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//ngram order longer than sentence - no point
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if (k > len) {
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continue;
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}
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for (size_t i = 0; i < len - k + 1; ++i) {
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ngram.clear();
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ngram.reserve(len);
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for (size_t j = i; j < i+k && j < len; ++j) {
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ngram.push_back(encoded_tokens[j]);
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}
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counts.Add(ngram);
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}
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}
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return len;
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}
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void BleuScorer::setReferenceFiles(const vector<string>& referenceFiles)
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{
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// Make sure reference data is clear
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m_references.reset();
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mert::VocabularyFactory::GetVocabulary()->clear();
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//load reference data
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for (size_t i = 0; i < referenceFiles.size(); ++i) {
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TRACE_ERR("Loading reference from " << referenceFiles[i] << endl);
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if (!OpenReference(referenceFiles[i].c_str(), i)) {
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throw runtime_error("Unable to open " + referenceFiles[i]);
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}
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}
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}
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bool BleuScorer::OpenReference(const char* filename, size_t file_id)
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{
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ifstream ifs(filename);
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if (!ifs) {
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cerr << "Cannot open " << filename << endl;
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return false;
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}
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return OpenReferenceStream(&ifs, file_id);
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}
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bool BleuScorer::OpenReferenceStream(istream* is, size_t file_id)
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{
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if (is == NULL) return false;
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string line;
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size_t sid = 0;
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while (getline(*is, line)) {
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line = preprocessSentence(line);
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if (file_id == 0) {
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Reference* ref = new Reference;
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m_references.push_back(ref); // Take ownership of the Reference object.
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}
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if (m_references.size() <= sid) {
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cerr << "Reference " << file_id << "has too many sentences." << endl;
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return false;
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}
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NgramCounts counts;
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size_t length = CountNgrams(line, counts, kBleuNgramOrder);
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//for any counts larger than those already there, merge them in
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for (NgramCounts::const_iterator ci = counts.begin(); ci != counts.end(); ++ci) {
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const NgramCounts::Key& ngram = ci->first;
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const NgramCounts::Value newcount = ci->second;
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NgramCounts::Value oldcount = 0;
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m_references[sid]->get_counts()->Lookup(ngram, &oldcount);
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if (newcount > oldcount) {
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m_references[sid]->get_counts()->operator[](ngram) = newcount;
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}
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}
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//add in the length
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m_references[sid]->push_back(length);
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if (sid > 0 && sid % 100 == 0) {
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TRACE_ERR(".");
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}
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++sid;
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}
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return true;
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}
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void BleuScorer::prepareStats(size_t sid, const string& text, ScoreStats& entry)
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{
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if (sid >= m_references.size()) {
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stringstream msg;
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msg << "Sentence id (" << sid << ") not found in reference set";
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throw runtime_error(msg.str());
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}
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NgramCounts testcounts;
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// stats for this line
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vector<ScoreStatsType> stats(kBleuNgramOrder * 2);
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string sentence = preprocessSentence(text);
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const size_t length = CountNgrams(sentence, testcounts, kBleuNgramOrder, true);
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const int reference_len = CalcReferenceLength(sid, length);
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stats.push_back(reference_len);
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//precision on each ngram type
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for (NgramCounts::const_iterator testcounts_it = testcounts.begin();
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testcounts_it != testcounts.end(); ++testcounts_it) {
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const NgramCounts::Value guess = testcounts_it->second;
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const size_t len = testcounts_it->first.size();
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NgramCounts::Value correct = 0;
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NgramCounts::Value v = 0;
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if (m_references[sid]->get_counts()->Lookup(testcounts_it->first, &v)) {
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correct = min(v, guess);
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}
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stats[len * 2 - 2] += correct;
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stats[len * 2 - 1] += guess;
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}
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entry.set(stats);
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}
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statscore_t BleuScorer::calculateScore(const vector<int>& comps) const
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{
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CHECK(comps.size() == kBleuNgramOrder * 2 + 1);
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float logbleu = 0.0;
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for (int i = 0; i < kBleuNgramOrder; ++i) {
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if (comps[2*i] == 0) {
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return 0.0;
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}
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logbleu += log(comps[2*i]) - log(comps[2*i+1]);
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}
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logbleu /= kBleuNgramOrder;
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// reflength divided by test length
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const float brevity = 1.0 - static_cast<float>(comps[kBleuNgramOrder * 2]) / comps[1];
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if (brevity < 0.0) {
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logbleu += brevity;
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}
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return exp(logbleu);
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}
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int BleuScorer::CalcReferenceLength(size_t sentence_id, size_t length)
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{
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switch (m_ref_length_type) {
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case AVERAGE:
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return m_references[sentence_id]->CalcAverage();
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break;
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case CLOSEST:
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return m_references[sentence_id]->CalcClosest(length);
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break;
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case SHORTEST:
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return m_references[sentence_id]->CalcShortest();
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break;
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default:
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cerr << "unknown reference types." << endl;
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exit(1);
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}
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}
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void BleuScorer::DumpCounts(ostream* os,
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const NgramCounts& counts) const
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{
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for (NgramCounts::const_iterator it = counts.begin();
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it != counts.end(); ++it) {
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*os << "(";
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const NgramCounts::Key& keys = it->first;
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for (size_t i = 0; i < keys.size(); ++i) {
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if (i != 0) {
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*os << " ";
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}
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*os << keys[i];
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}
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*os << ") : " << it->second << ", ";
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}
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*os << endl;
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}
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float smoothedSentenceBleu
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(const std::vector<float>& stats, float smoothing, bool smoothBP)
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{
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CHECK(stats.size() == kBleuNgramOrder * 2 + 1);
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float logbleu = 0.0;
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for (int j = 0; j < kBleuNgramOrder; j++) {
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logbleu += log(stats[2 * j] + smoothing) - log(stats[2 * j + 1] + smoothing);
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}
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logbleu /= kBleuNgramOrder;
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const float reflength = stats[(kBleuNgramOrder * 2)] +
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(smoothBP ? smoothing : 0.0f);
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const float brevity = 1.0 - reflength / stats[1];
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if (brevity < 0.0) {
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logbleu += brevity;
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}
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return exp(logbleu);
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}
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float sentenceLevelBackgroundBleu(const std::vector<float>& sent, const std::vector<float>& bg)
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{
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// Sum sent and background
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std::vector<float> stats;
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CHECK(sent.size()==bg.size());
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CHECK(sent.size()==kBleuNgramOrder*2+1);
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for(size_t i=0; i<sent.size(); i++)
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stats.push_back(sent[i]+bg[i]);
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// Calculate BLEU
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float logbleu = 0.0;
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for (int j = 0; j < kBleuNgramOrder; j++) {
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logbleu += log(stats[2 * j]) - log(stats[2 * j + 1]);
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}
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logbleu /= kBleuNgramOrder;
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const float brevity = 1.0 - stats[(kBleuNgramOrder * 2)] / stats[1];
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if (brevity < 0.0) {
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logbleu += brevity;
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}
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// Exponentiate and scale by reference length (as per Chiang et al 08)
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return exp(logbleu) * stats[kBleuNgramOrder*2];
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}
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float unsmoothedBleu(const std::vector<float>& stats)
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{
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CHECK(stats.size() == kBleuNgramOrder * 2 + 1);
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float logbleu = 0.0;
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for (int j = 0; j < kBleuNgramOrder; j++) {
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logbleu += log(stats[2 * j]) - log(stats[2 * j + 1]);
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}
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logbleu /= kBleuNgramOrder;
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const float brevity = 1.0 - stats[(kBleuNgramOrder * 2)] / stats[1];
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if (brevity < 0.0) {
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logbleu += brevity;
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}
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return exp(logbleu);
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}
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vector<float> BleuScorer::ScoreNbestList(const string& scoreFile, const string& featureFile)
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{
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vector<string> scoreFiles;
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vector<string> featureFiles;
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scoreFiles.push_back(scoreFile);
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featureFiles.push_back(featureFile);
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vector<FeatureDataIterator> featureDataIters;
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vector<ScoreDataIterator> scoreDataIters;
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for (size_t i = 0; i < featureFiles.size(); ++i) {
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featureDataIters.push_back(FeatureDataIterator(featureFiles[i]));
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scoreDataIters.push_back(ScoreDataIterator(scoreFiles[i]));
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}
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vector<pair<size_t,size_t> > hypotheses;
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if (featureDataIters[0] == FeatureDataIterator::end()) {
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cerr << "Error: at the end of feature data iterator" << endl;
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exit(1);
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}
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for (size_t i = 0; i < featureFiles.size(); ++i) {
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if (featureDataIters[i] == FeatureDataIterator::end()) {
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cerr << "Error: Feature file " << i << " ended prematurely" << endl;
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exit(1);
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}
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if (scoreDataIters[i] == ScoreDataIterator::end()) {
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cerr << "Error: Score file " << i << " ended prematurely" << endl;
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exit(1);
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}
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if (featureDataIters[i]->size() != scoreDataIters[i]->size()) {
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cerr << "Error: features and scores have different size" << endl;
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exit(1);
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}
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for (size_t j = 0; j < featureDataIters[i]->size(); ++j) {
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hypotheses.push_back(pair<size_t,size_t>(i,j));
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}
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}
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// score the nbest list
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vector<float> bleuScores;
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for (size_t i=0; i < hypotheses.size(); ++i) {
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pair<size_t,size_t> translation = hypotheses[i];
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float bleu = smoothedSentenceBleu(scoreDataIters[translation.first]->operator[](translation.second));
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bleuScores.push_back(bleu);
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}
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return bleuScores;
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}
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}
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