From 0a47dda0e980d1fb6222a1f548649914427555b2 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Fri, 23 Sep 2011 22:02:45 +0200 Subject: more renaming, random pair sampler uses boost rng --- dtrain/score.cc | 165 +++++++++++++++++++++++++------------------------------- 1 file changed, 74 insertions(+), 91 deletions(-) (limited to 'dtrain/score.cc') diff --git a/dtrain/score.cc b/dtrain/score.cc index 1e98c11d..d08e87f3 100644 --- a/dtrain/score.cc +++ b/dtrain/score.cc @@ -1,166 +1,149 @@ #include "score.h" - namespace dtrain { -/****************************************************************************** - * NGRAMS - * - * - * make_ngrams - * - */ -typedef map, size_t> Ngrams; Ngrams -make_ngrams( vector& s, size_t N ) +make_ngrams(vector& s, size_t N) { Ngrams ngrams; vector ng; - for ( size_t i = 0; i < s.size(); i++ ) { + for (size_t i = 0; i < s.size(); i++) { ng.clear(); - for ( size_t j = i; j < min( i+N, s.size() ); j++ ) { - ng.push_back( s[j] ); + for (size_t j = i; j < min(i+N, s.size()); j++) { + ng.push_back(s[j]); ngrams[ng]++; } } return ngrams; } - -/* - * ngram_matches - * - */ NgramCounts -make_ngram_counts( vector hyp, vector ref, size_t N ) +make_ngram_counts(vector hyp, vector ref, size_t N) { - Ngrams hyp_ngrams = make_ngrams( hyp, N ); - Ngrams ref_ngrams = make_ngrams( ref, N ); - NgramCounts counts( N ); + Ngrams hyp_ngrams = make_ngrams(hyp, N); + Ngrams ref_ngrams = make_ngrams(ref, N); + NgramCounts counts(N); Ngrams::iterator it; Ngrams::iterator ti; - for ( it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++ ) { - ti = ref_ngrams.find( it->first ); - if ( ti != ref_ngrams.end() ) { - counts.add( it->second, ti->second, it->first.size() - 1 ); + for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) { + ti = ref_ngrams.find(it->first); + if (ti != ref_ngrams.end()) { + counts.add(it->second, ti->second, it->first.size() - 1); } else { - counts.add( it->second, 0, it->first.size() - 1 ); + counts.add(it->second, 0, it->first.size() - 1); } } return counts; } - -/****************************************************************************** - * SCORERS - * +/* + * bleu * - * brevity_penaly + * as in "BLEU: a Method for Automatic Evaluation + * of Machine Translation" + * (Papineni et al. '02) * + * NOTE: 0 if one n in {1..N} has 0 count */ double -brevity_penaly( const size_t hyp_len, const size_t ref_len ) +brevity_penaly(const size_t hyp_len, const size_t ref_len) { - if ( hyp_len > ref_len ) return 1; - return exp( 1 - (double)ref_len/(double)hyp_len ); + if (hyp_len > ref_len) return 1; + return exp(1 - (double)ref_len/(double)hyp_len); } - - -/* - * bleu - * as in "BLEU: a Method for Automatic Evaluation of Machine Translation" (Papineni et al. '02) - * page TODO - * 0 if for N one of the counts = 0 - */ double -bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, - size_t N, vector weights ) +bleu(NgramCounts& counts, const size_t hyp_len, const size_t ref_len, + size_t N, vector weights ) { - if ( hyp_len == 0 || ref_len == 0 ) return 0; - if ( ref_len < N ) N = ref_len; + if (hyp_len == 0 || ref_len == 0) return 0; + if (ref_len < N) N = ref_len; float N_ = (float)N; - if ( weights.empty() ) + if (weights.empty()) { - for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ ); + for (size_t i = 0; i < N; i++) weights.push_back(1/N_); } double sum = 0; - for ( size_t i = 0; i < N; i++ ) { - if ( counts.clipped[i] == 0 || counts.sum[i] == 0 ) return 0; - sum += weights[i] * log( (double)counts.clipped[i] / (double)counts.sum[i] ); + for (size_t i = 0; i < N; i++) { + if (counts.clipped[i] == 0 || counts.sum[i] == 0) return 0; + sum += weights[i] * log((double)counts.clipped[i] / (double)counts.sum[i]); } - return brevity_penaly( hyp_len, ref_len ) * exp( sum ); + return brevity_penaly(hyp_len, ref_len) * exp(sum); } - /* - * stupid_bleu - * as in "ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation (Lin & Och '04) - * page TODO - * 0 iff no 1gram match + * 'stupid' bleu + * + * as in "ORANGE: a Method for Evaluating + * Automatic Evaluation Metrics + * for Machine Translation" + * (Lin & Och '04) + * + * NOTE: 0 iff no 1gram match */ double -stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, - size_t N, vector weights ) +stupid_bleu(NgramCounts& counts, const size_t hyp_len, const size_t ref_len, + size_t N, vector weights ) { - if ( hyp_len == 0 || ref_len == 0 ) return 0; - if ( ref_len < N ) N = ref_len; + if (hyp_len == 0 || ref_len == 0) return 0; + if (ref_len < N) N = ref_len; float N_ = (float)N; - if ( weights.empty() ) + if (weights.empty()) { - for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ ); + for (size_t i = 0; i < N; i++) weights.push_back(1/N_); } double sum = 0; float add = 0; - for ( size_t i = 0; i < N; i++ ) { - if ( i == 1 ) add = 1; - sum += weights[i] * log( ((double)counts.clipped[i] + add) / ((double)counts.sum[i] + add) ); + for (size_t i = 0; i < N; i++) { + if (i == 1) add = 1; + sum += weights[i] * log(((double)counts.clipped[i] + add) / ((double)counts.sum[i] + add)); } - return brevity_penaly( hyp_len, ref_len ) * exp( sum ); + return brevity_penaly(hyp_len, ref_len) * exp(sum); } - /* - * smooth_bleu - * as in "An End-to-End Discriminative Approach to Machine Translation" (Liang et al. '06) - * page TODO - * max. 0.9375 + * smooth bleu + * + * as in "An End-to-End Discriminative Approach + * to Machine Translation" + * (Liang et al. '06) + * + * NOTE: max is 0.9375 */ double -smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, - const size_t N, vector weights ) +smooth_bleu(NgramCounts& counts, const size_t hyp_len, const size_t ref_len, + const size_t N, vector weights ) { - if ( hyp_len == 0 || ref_len == 0 ) return 0; + if (hyp_len == 0 || ref_len == 0) return 0; float N_ = (float)N; - if ( weights.empty() ) + if (weights.empty()) { - for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ ); + for (size_t i = 0; i < N; i++) weights.push_back(1/N_); } double sum = 0; float j = 1; - for ( size_t i = 0; i < N; i++ ) { - if ( counts.clipped[i] == 0 || counts.sum[i] == 0) continue; - sum += exp((weights[i] * log((double)counts.clipped[i]/(double)counts.sum[i]))) / pow( 2, N_-j+1 ); + for (size_t i = 0; i < N; i++) { + if (counts.clipped[i] == 0 || counts.sum[i] == 0) continue; + sum += exp((weights[i] * log((double)counts.clipped[i]/(double)counts.sum[i]))) / pow(2, N_-j+1); j++; } - return brevity_penaly( hyp_len, ref_len ) * sum; + return brevity_penaly(hyp_len, ref_len) * sum; } - /* - * approx_bleu - * as in "Online Large-Margin Training for Statistical Machine Translation" (Watanabe et al. '07) - * CHIANG, RESNIK, synt struct features - * .9* - * page TODO + * approx. bleu * + * as in "Online Large-Margin Training of Syntactic + * and Structural Translation Features" + * (Chiang et al. '08) */ double -approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len, - const size_t N, vector weights ) +approx_bleu(NgramCounts& counts, const size_t hyp_len, const size_t ref_len, + const size_t N, vector weights) { - return bleu( counts, hyp_len, ref_len, N, weights ); + return brevity_penaly(hyp_len, ref_len) + * 0.9 * bleu(counts, hyp_len, ref_len, N, weights); } -- cgit v1.2.3