From 4d8c300734c441821141f4bff044c439e004ff84 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Sun, 25 Sep 2011 21:43:57 +0200 Subject: kbest, ksampler refactoring --- dtrain/dtrain.cc | 48 ++++++++++++++++++------------------ dtrain/kbestget.h | 55 ++++++++++++++++++++---------------------- dtrain/ksampler.h | 33 ++++++++++++------------- dtrain/pairsampling.h | 48 +++++++++++++----------------------- dtrain/test/example/dtrain.ini | 8 +++--- 5 files changed, 87 insertions(+), 105 deletions(-) (limited to 'dtrain') diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc index a70ca2f1..ad1ab7b7 100644 --- a/dtrain/dtrain.cc +++ b/dtrain/dtrain.cc @@ -10,11 +10,11 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("output", po::value()->default_value("-"), "output weights file (or VOID)") ("input_weights", po::value(), "input weights file (e.g. from previous iteration)") ("decoder_config", po::value(), "configuration file for cdec") - ("ksamples", po::value()->default_value(100), "size of kbest or sample from forest") + ("k", po::value()->default_value(100), "size of kbest or sample from forest") ("sample_from", po::value()->default_value("kbest"), "where to get translations from") ("filter", po::value()->default_value("unique"), "filter kbest list") ("pair_sampling", po::value()->default_value("all"), "how to sample pairs: all, rand") - ("ngrams", po::value()->default_value(3), "N for Ngrams") + ("N", po::value()->default_value(3), "N for Ngrams") ("epochs", po::value()->default_value(2), "# of iterations T") ("scorer", po::value()->default_value("stupid_bleu"), "scoring metric") ("stop_after", po::value()->default_value(0), "stop after X input sentences") @@ -75,8 +75,8 @@ main(int argc, char** argv) hstreaming = true; quiet = true; } - const size_t k = cfg["ksamples"].as(); - const size_t N = cfg["ngrams"].as(); + const size_t k = cfg["k"].as(); + const size_t N = cfg["N"].as(); const size_t T = cfg["epochs"].as(); const size_t stop_after = cfg["stop_after"].as(); const string filter_type = cfg["filter"].as(); @@ -96,7 +96,7 @@ main(int argc, char** argv) MT19937 rng; // random number generator // setup decoder observer - HypoSampler* observer; + HypSampler* observer; if (sample_from == "kbest") { observer = dynamic_cast(new KBestGetter(k, filter_type)); } else { @@ -274,45 +274,45 @@ main(int argc, char** argv) decoder.Decode(src_str_buf[ii], observer); } - Samples* samples = observer->GetSamples(); + vector* samples = observer->GetSamples(); // (local) scoring if (t > 0) ref_ids = ref_ids_buf[ii]; score_t score = 0.; - for (size_t i = 0; i < samples->GetSize(); i++) { - NgramCounts counts = make_ngram_counts(ref_ids, samples->sents[i], N); + for (size_t i = 0; i < samples->size(); i++) { + NgramCounts counts = make_ngram_counts(ref_ids, (*samples)[i].w, N); if (scorer_str == "approx_bleu") { size_t hyp_len = 0; if (i == 0) { // 'context of 1best translations' global_counts += counts; - global_hyp_len += samples->sents[i].size(); + global_hyp_len += (*samples)[i].w.size(); global_ref_len += ref_ids.size(); counts.reset(); } else { - hyp_len = samples->sents[i].size(); + hyp_len = (*samples)[i].w.size(); } - NgramCounts counts_tmp = global_counts + counts; - score = .9 * scorer(counts_tmp, + NgramCounts _c = global_counts + counts; + score = .9 * scorer(_c, global_ref_len, global_hyp_len + hyp_len, N, bleu_weights); } else { score = scorer(counts, ref_ids.size(), - samples->sents[i].size(), N, bleu_weights); + (*samples)[i].w.size(), N, bleu_weights); } - samples->scores.push_back(score); + (*samples)[i].score = (score); if (i == 0) { score_sum += score; - model_sum += samples->model_scores[i]; + model_sum += (*samples)[i].model; } if (verbose) { if (i == 0) cout << "'" << TD::GetString(ref_ids) << "' [ref]" << endl; - cout << _p5 << _np << "[hyp " << i << "] " << "'" << TD::GetString(samples->sents[i]) << "'"; - cout << " [SCORE=" << score << ",model="<< samples->model_scores[i] << "]" << endl; - cout << samples->feats[i] << endl; + cout << _p5 << _np << "[hyp " << i << "] " << "'" << TD::GetString((*samples)[i].w) << "'"; + cout << " [SCORE=" << score << ",model="<< (*samples)[i].model << "]" << endl; + cout << (*samples)[i].f << endl; } } // sample/scoring loop @@ -321,18 +321,18 @@ main(int argc, char** argv) ////////////////////////////////////////////////////////// // UPDATE WEIGHTS if (!noup) { - vector pairs; + vector > pairs; if (pair_sampling == "all") sample_all_pairs(samples, pairs); if (pair_sampling == "rand") sample_rand_pairs(samples, pairs, &rng); - for (vector::iterator ti = pairs.begin(); + for (vector >::iterator ti = pairs.begin(); ti != pairs.end(); ti++) { SparseVector dv; - if (ti->first_score - ti->second_score < 0) { - dv = ti->second - ti->first; + if (ti->first.score - ti->second.score < 0) { + dv = ti->second.f - ti->first.f; //} else { //dv = ti->first - ti->second; //} @@ -344,14 +344,14 @@ main(int argc, char** argv) lambdas += dv * eta; if (verbose) { - cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl; + /*cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl; cout << " i " << TD::GetString(samples->sents[ti->first_rank]) << endl; cout << " " << samples->feats[ti->first_rank] << endl; cout << " j " << TD::GetString(samples->sents[ti->second_rank]) << endl; cout << " " << samples->feats[ti->second_rank] << endl; cout << " diff vec: " << dv << endl; cout << " lambdas after update: " << lambdas << endl; - cout << "}}" << endl; + cout << "}}" << endl;*/ } } else { //SparseVector reg; diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h index 79201182..403384de 100644 --- a/dtrain/kbestget.h +++ b/dtrain/kbestget.h @@ -7,28 +7,27 @@ namespace dtrain { -struct Samples +struct ScoredHyp { - vector > feats; - vector > sents; - vector model_scores; - vector scores; - size_t GetSize() { return sents.size(); } + vector w; + SparseVector f; + score_t model; + score_t score; }; -struct HypoSampler : public DecoderObserver +struct HypSampler : public DecoderObserver { - virtual Samples* GetSamples() {} + virtual vector* GetSamples() {} }; -struct KBestGetter : public HypoSampler +struct KBestGetter : public HypSampler { const size_t k_; - const string filter_type; - Samples s; + const string filter_type_; + vector s_; KBestGetter(const size_t k, const string filter_type) : - k_(k), filter_type(filter_type) {} + k_(k), filter_type_(filter_type) {} virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) @@ -36,14 +35,14 @@ struct KBestGetter : public HypoSampler KBest(*hg); } - Samples* GetSamples() { return &s; } + vector* GetSamples() { return &s_; } void KBest(const Hypergraph& forest) { - if (filter_type == "unique") { + if (filter_type_ == "unique") { KBestUnique(forest); - } else if (filter_type == "no") { + } else if (filter_type_ == "no") { KBestNoFilter(forest); } } @@ -51,36 +50,34 @@ struct KBestGetter : public HypoSampler void KBestUnique(const Hypergraph& forest) { - s.sents.clear(); - s.feats.clear(); - s.model_scores.clear(); - s.scores.clear(); + s_.clear(); KBest::KBestDerivations, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_); for (size_t i = 0; i < k_; ++i) { const KBest::KBestDerivations, ESentenceTraversal, KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d = kbest.LazyKthBest(forest.nodes_.size() - 1, i); if (!d) break; - s.sents.push_back(d->yield); - s.feats.push_back(d->feature_values); - s.model_scores.push_back(log(d->score)); + ScoredHyp h; + h.w = d->yield; + h.f = d->feature_values; + h.model = log(d->score); + s_.push_back(h); } } void KBestNoFilter(const Hypergraph& forest) { - s.sents.clear(); - s.feats.clear(); - s.model_scores.clear(); - s.scores.clear(); + s_.clear(); KBest::KBestDerivations, ESentenceTraversal> kbest(forest, k_); for (size_t i = 0; i < k_; ++i) { const KBest::KBestDerivations, ESentenceTraversal>::Derivation* d = kbest.LazyKthBest(forest.nodes_.size() - 1, i); if (!d) break; - s.sents.push_back(d->yield); - s.feats.push_back(d->feature_values); - s.model_scores.push_back(log(d->score)); + ScoredHyp h; + h.w = d->yield; + h.f = d->feature_values; + h.model = log(d->score); + s_.push_back(h); } } }; diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h index ac88b643..bbe2b402 100644 --- a/dtrain/ksampler.h +++ b/dtrain/ksampler.h @@ -13,34 +13,33 @@ namespace dtrain * KSampler * */ -struct KSampler : public HypoSampler +struct KSampler : public HypSampler { const size_t k_; - Samples s; - MT19937* rng; + vector s_; + MT19937* prng_; - explicit KSampler( const size_t k, MT19937* prng ) : - k_(k), rng(prng) {} + explicit KSampler(const size_t k, MT19937* prng) : + k_(k), prng_(prng) {} virtual void - NotifyTranslationForest( const SentenceMetadata& smeta, Hypergraph* hg ) + NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) { - Sample( *hg ); + Sample(*hg); } - Samples* GetSamples() { return &s; } + vector* GetSamples() { return &s_; } - void Sample( const Hypergraph& forest ) { - s.sents.clear(); - s.feats.clear(); - s.model_scores.clear(); - s.scores.clear(); + void Sample(const Hypergraph& forest) { + s_.clear(); std::vector samples; - HypergraphSampler::sample_hypotheses(forest, k_, rng, &samples); + HypergraphSampler::sample_hypotheses(forest, k_, prng_, &samples); for ( size_t i = 0; i < k_; ++i ) { - s.sents.push_back( samples[i].words ); - s.feats.push_back( samples[i].fmap ); - s.model_scores.push_back( log(samples[i].model_score) ); + ScoredHyp h; + h.w = samples[i].words; + h.f = samples[i].fmap; + h.model = log(samples[i].model_score); + s_.push_back(h); } } }; diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h index a8521485..2e4ab155 100644 --- a/dtrain/pairsampling.h +++ b/dtrain/pairsampling.h @@ -8,47 +8,33 @@ namespace dtrain { -struct Pair -{ - SparseVector first, second; - size_t first_rank, second_rank; - double first_score, second_score; -}; - inline void -sample_all_pairs(Samples* kb, vector &training) +sample_all_pairs(vector* s, vector > &training) { - for (size_t i = 0; i < kb->GetSize()-1; i++) { - for (size_t j = i+1; j < kb->GetSize(); j++) { - Pair p; - p.first = kb->feats[i]; - p.second = kb->feats[j]; - p.first_rank = i; - p.second_rank = j; - p.first_score = kb->scores[i]; - p.second_score = kb->scores[j]; + for (size_t i = 0; i < s->size()-1; i++) { + for (size_t j = i+1; j < s->size(); j++) { + pair p; + p.first = (*s)[i]; + p.second = (*s)[j]; training.push_back(p); - } // j - } // i + } + } } inline void -sample_rand_pairs(Samples* kb, vector &training, MT19937* prng) +sample_rand_pairs(vector* s, vector > &training, + MT19937* prng) { - for (size_t i = 0; i < kb->GetSize()-1; i++) { - for (size_t j = i+1; j < kb->GetSize(); j++) { + for (size_t i = 0; i < s->size()-1; i++) { + for (size_t j = i+1; j < s->size(); j++) { if (prng->next() < .5) { - Pair p; - p.first = kb->feats[i]; - p.second = kb->feats[j]; - p.first_rank = i; - p.second_rank = j; - p.first_score = kb->scores[i]; - p.second_score = kb->scores[j]; + pair p; + p.first = (*s)[i]; + p.second = (*s)[j]; training.push_back(p); } - } // j - } // i + } + } } diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini index aee3c89e..00ba72f9 100644 --- a/dtrain/test/example/dtrain.ini +++ b/dtrain/test/example/dtrain.ini @@ -1,11 +1,11 @@ decoder_config=test/example/cdec.ini -ksamples=100 -ngrams=3 +k=100 +N=3 epochs=1000 input=test/example/nc-1k.gz scorer=stupid_bleu output=test/example/weights.gz -stop_after=10 -sample_from=kbest +stop_after=100 +sample_from=forest pair_sampling=all print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PassThrough -- cgit v1.2.3