diff options
Diffstat (limited to 'dtrain')
-rw-r--r-- | dtrain/Makefile.am | 2 | ||||
-rw-r--r-- | dtrain/README.md | 10 | ||||
-rw-r--r-- | dtrain/dtrain.cc | 97 | ||||
-rw-r--r-- | dtrain/dtrain.h | 14 | ||||
-rw-r--r-- | dtrain/ksampler.h | 7 | ||||
-rw-r--r-- | dtrain/pairsampling.h | 49 | ||||
-rw-r--r-- | dtrain/score.cc | 117 | ||||
-rw-r--r-- | dtrain/score.h | 64 | ||||
-rw-r--r-- | dtrain/test/example/README | 4 | ||||
-rw-r--r-- | dtrain/test/example/dtrain.ini | 3 | ||||
-rw-r--r-- | dtrain/test/example/expected-output | 125 |
11 files changed, 421 insertions, 71 deletions
diff --git a/dtrain/Makefile.am b/dtrain/Makefile.am index f39d161e..64fef489 100644 --- a/dtrain/Makefile.am +++ b/dtrain/Makefile.am @@ -3,5 +3,5 @@ bin_PROGRAMS = dtrain dtrain_SOURCES = dtrain.cc score.cc dtrain_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz -AM_CPPFLAGS = -O3 -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval +AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval diff --git a/dtrain/README.md b/dtrain/README.md index 9580df6d..7edabbf1 100644 --- a/dtrain/README.md +++ b/dtrain/README.md @@ -39,16 +39,6 @@ For an example of local usage (with the 'distributed' format) the see test/example/ . This expects dtrain to be built without DTRAIN_LOCAL. -Next ----- -+ (dtrain|decoder) more meta-parameters testing -+ feature selection directly in dtrain -+ feature template: target side rule ngrams -+ sa-extract -> leave-one-out for grammar of training set? -+ make svm doable; no subgradient? -+ reranking while sgd? -+ try PRO, mira emulations - Legal ----- Copyright (c) 2012 by Patrick Simianer <p@simianer.de> diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc index d9bce843..b3e62914 100644 --- a/dtrain/dtrain.cc +++ b/dtrain/dtrain.cc @@ -6,37 +6,39 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) { po::options_description ini("Configuration File Options"); ini.add_options() - ("input", po::value<string>()->default_value("-"), "input file") - ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT") - ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)") - ("decoder_config", po::value<string>(), "configuration file for cdec") - ("print_weights", po::value<string>(), "weights to print on each iteration") - ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences") - ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use") - ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration") - ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id") - ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)") - ("k", po::value<unsigned>()->default_value(100), "how many translations to sample") - ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'") - ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'") - ("pair_sampling", po::value<string>()->default_value("XYX"), "how to sample pairs: 'all', 'XYX' or 'PRO'") - ("hi_lo", po::value<float>()->default_value(0.1), "hi and lo (X) for XYX (default 0.1), <= 0.5") - ("pair_threshold", po::value<score_t>()->default_value(0.), "bleu [0,1] threshold to filter pairs") - ("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)") - ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_") - ("learning_rate", po::value<weight_t>()->default_value(0.0001), "learning rate") - ("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)") - ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)") - ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input") - ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)") - ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength") - ("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent (or by threshold) of features after each epoch") - ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU") - ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair") + ("input", po::value<string>()->default_value("-"), "input file") + ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT") + ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)") + ("decoder_config", po::value<string>(), "configuration file for cdec") + ("print_weights", po::value<string>(), "weights to print on each iteration") + ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences") + ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use") + ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration") + ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id") + ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)") + ("k", po::value<unsigned>()->default_value(100), "how many translations to sample") + ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'") + ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'") + ("pair_sampling", po::value<string>()->default_value("XYX"), "how to sample pairs: 'all', 'XYX' or 'PRO'") + ("hi_lo", po::value<float>()->default_value(0.1), "hi and lo (X) for XYX (default 0.1), <= 0.5") + ("pair_threshold", po::value<score_t>()->default_value(0.), "bleu [0,1] threshold to filter pairs") + ("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)") + ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_, lc_") + ("learning_rate", po::value<weight_t>()->default_value(0.0001), "learning rate") + ("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)") + ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)") + ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input") + ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)") + ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength") + ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPL") // TODO + ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU") + ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair") + ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near") + ("max_pairs", po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.") #ifdef DTRAIN_LOCAL - ("refs,r", po::value<string>(), "references in local mode") + ("refs,r", po::value<string>(), "references in local mode") #endif - ("noup", po::value<bool>()->zero_tokens(), "do not update weights"); + ("noup", po::value<bool>()->zero_tokens(), "do not update weights"); po::options_description cl("Command Line Options"); cl.add_options() ("config,c", po::value<string>(), "dtrain config file") @@ -134,6 +136,9 @@ main(int argc, char** argv) const string select_weights = cfg["select_weights"].as<string>(); const float hi_lo = cfg["hi_lo"].as<float>(); const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>(); + const unsigned max_pairs = cfg["max_pairs"].as<unsigned>(); + weight_t loss_margin = cfg["loss_margin"].as<weight_t>(); + if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max(); bool scale_bleu_diff = false; if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true; bool average = false; @@ -160,8 +165,16 @@ main(int argc, char** argv) scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer); } else if (scorer_str == "smooth_bleu") { scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer); + } else if (scorer_str == "sum_bleu") { + scorer = dynamic_cast<SumBleuScorer*>(new SumBleuScorer); + } else if (scorer_str == "sumexp_bleu") { + scorer = dynamic_cast<SumExpBleuScorer*>(new SumExpBleuScorer); + } else if (scorer_str == "sumwhatever_bleu") { + scorer = dynamic_cast<SumWhateverBleuScorer*>(new SumWhateverBleuScorer); } else if (scorer_str == "approx_bleu") { scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d)); + } else if (scorer_str == "lc_bleu") { + scorer = dynamic_cast<LinearBleuScorer*>(new LinearBleuScorer(N)); } else { cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl; exit(1); @@ -220,7 +233,7 @@ main(int argc, char** argv) grammar_buf_out.open(grammar_buf_fn.c_str()); #endif - unsigned in_sz = UINT_MAX; // input index, input size + unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size vector<pair<score_t, score_t> > all_scores; score_t max_score = 0.; unsigned best_it = 0; @@ -242,6 +255,7 @@ main(int argc, char** argv) if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl; else cerr << setw(25) << "learning rate " << "bleu diff" << endl; cerr << setw(25) << "gamma " << gamma << endl; + cerr << setw(25) << "loss margin " << loss_margin << endl; cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl; if (pair_sampling == "XYX") cerr << setw(25) << "hi lo " << hi_lo << endl; @@ -251,6 +265,7 @@ main(int argc, char** argv) cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl; if (rescale) cerr << setw(25) << "rescale " << rescale << endl; + cerr << setw(25) << "max pairs " << max_pairs << endl; cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; cerr << setw(25) << "input " << "'" << input_fn << "'" << endl; #ifdef DTRAIN_LOCAL @@ -415,21 +430,27 @@ main(int argc, char** argv) // get pairs vector<pair<ScoredHyp,ScoredHyp> > pairs; if (pair_sampling == "all") - all_pairs(samples, pairs, pair_threshold); + all_pairs(samples, pairs, pair_threshold, max_pairs); if (pair_sampling == "XYX") - partXYX(samples, pairs, pair_threshold, hi_lo); + partXYX(samples, pairs, pair_threshold, max_pairs, hi_lo); if (pair_sampling == "PRO") - PROsampling(samples, pairs, pair_threshold); + PROsampling(samples, pairs, pair_threshold, max_pairs); npairs += pairs.size(); for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + bool rank_error = true; // pair sampling already did this for us + rank_errors++; + score_t margin = std::numeric_limits<float>::max(); +#else bool rank_error = it->first.model <= it->second.model; if (rank_error) rank_errors++; - score_t margin = fabs(it->first.model - it->second.model); - if (!rank_error && margin < 1) margin_violations++; + score_t margin = fabs(fabs(it->first.model) - fabs(it->second.model)); + if (!rank_error && margin < loss_margin) margin_violations++; +#endif if (scale_bleu_diff) eta = it->first.score - it->second.score; - if (rank_error || (gamma && margin<1)) { + if (rank_error || margin < loss_margin) { SparseVector<weight_t> diff_vec = it->first.f - it->second.f; lambdas.plus_eq_v_times_s(diff_vec, eta); if (gamma) @@ -486,7 +507,7 @@ main(int argc, char** argv) if (average) w_average += lambdas; - if (scorer_str == "approx_bleu") scorer->Reset(); + if (scorer_str == "approx_bleu" || scorer_str == "lc_bleu") scorer->Reset(); if (t == 0) { in_sz = ii; // remember size of input (# lines) @@ -534,8 +555,10 @@ main(int argc, char** argv) cerr << _np << npairs/(float)in_sz << endl; cerr << " avg # rank err: "; cerr << rank_errors/(float)in_sz << endl; +#ifndef DTRAIN_FASTER_PERCEPTRON cerr << " avg # margin viol: "; cerr << margin_violations/(float)in_sz << endl; +#endif cerr << " non0 feature count: " << nonz << endl; cerr << " avg list sz: " << list_sz/(float)in_sz << endl; cerr << " avg f count: " << f_count/(float)list_sz << endl; diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h index 94d149ce..7e084a79 100644 --- a/dtrain/dtrain.h +++ b/dtrain/dtrain.h @@ -1,6 +1,14 @@ #ifndef _DTRAIN_H_ #define _DTRAIN_H_ +#undef DTRAIN_FASTER_PERCEPTRON // only look at misranked pairs + // DO NOT USE WITH SVM! +#define DTRAIN_LOCAL +#define DTRAIN_DOTS 10 // after how many inputs to display a '.' +#define DTRAIN_GRAMMAR_DELIM "########EOS########" +#define DTRAIN_SCALE 100000 + + #include <iomanip> #include <climits> #include <string.h> @@ -13,11 +21,7 @@ #include "filelib.h" -#undef DTRAIN_LOCAL -#define DTRAIN_DOTS 10 // after how many inputs to display a '.' -#define DTRAIN_GRAMMAR_DELIM "########EOS########" -#define DTRAIN_SCALE 100000 using namespace std; using namespace dtrain; @@ -32,7 +36,7 @@ inline void register_and_convert(const vector<string>& strs, vector<WordID>& ids inline string gettmpf(const string path, const string infix) { - char fn[1024]; + char fn[path.size() + infix.size() + 8]; strcpy(fn, path.c_str()); strcat(fn, "/"); strcat(fn, infix.c_str()); diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h index f52fb649..bc2f56cd 100644 --- a/dtrain/ksampler.h +++ b/dtrain/ksampler.h @@ -8,6 +8,11 @@ namespace dtrain { +bool +cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b) +{ + return a.model > b.model; +} struct KSampler : public HypSampler { @@ -44,6 +49,8 @@ struct KSampler : public HypSampler sz_++; f_count_ += h.f.size(); } + sort(s_.begin(), s_.end(), cmp_hyp_by_model_d); + for (unsigned i = 0; i < s_.size(); i++) s_[i].rank = i; } }; diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h index bac132c6..84be1efb 100644 --- a/dtrain/pairsampling.h +++ b/dtrain/pairsampling.h @@ -19,10 +19,12 @@ cmp_hyp_by_score_d(ScoredHyp a, ScoredHyp b) } inline void -all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused=1) +all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float _unused=1) { sort(s->begin(), s->end(), cmp_hyp_by_score_d); unsigned sz = s->size(); + bool b = false; + unsigned count = 0; for (unsigned i = 0; i < sz-1; i++) { for (unsigned j = i+1; j < sz; j++) { if (threshold > 0) { @@ -32,7 +34,12 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc if ((*s)[i].score != (*s)[j].score) training.push_back(make_pair((*s)[i], (*s)[j])); } + if (++count == max) { + b = true; + break; + } } + if (b) break; } } @@ -44,13 +51,22 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc */ inline void -partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float hi_lo) +partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float hi_lo) { - sort(s->begin(), s->end(), cmp_hyp_by_score_d); unsigned sz = s->size(); + if (sz < 2) return; + sort(s->begin(), s->end(), cmp_hyp_by_score_d); unsigned sep = round(sz*hi_lo); - for (unsigned i = 0; i < sep; i++) { - for (unsigned j = sep; j < sz; j++) { + unsigned sep_hi = sep; + if (sz > 4) while (sep_hi < sz && (*s)[sep_hi-1].score == (*s)[sep_hi].score) ++sep_hi; + else sep_hi = 1; + bool b = false; + unsigned count = 0; + for (unsigned i = 0; i < sep_hi; i++) { + for (unsigned j = sep_hi; j < sz; j++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + if ((*s)[i].model <= (*s)[j].model) { +#endif if (threshold > 0) { if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) training.push_back(make_pair((*s)[i], (*s)[j])); @@ -58,10 +74,23 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor if ((*s)[i].score != (*s)[j].score) training.push_back(make_pair((*s)[i], (*s)[j])); } + if (++count == max) { + b = true; + break; + } +#ifdef DTRAIN_FASTER_PERCEPTRON + } +#endif } + if (b) break; } - for (unsigned i = sep; i < sz-sep; i++) { - for (unsigned j = sz-sep; j < sz; j++) { + unsigned sep_lo = sz-sep; + while (sep_lo > 0 && (*s)[sep_lo-1].score == (*s)[sep_lo].score) --sep_lo; + for (unsigned i = sep_hi; i < sz-sep_lo; i++) { + for (unsigned j = sz-sep_lo; j < sz; j++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + if ((*s)[i].model <= (*s)[j].model) { +#endif if (threshold > 0) { if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) training.push_back(make_pair((*s)[i], (*s)[j])); @@ -69,6 +98,10 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor if ((*s)[i].score != (*s)[j].score) training.push_back(make_pair((*s)[i], (*s)[j])); } + if (++count == max) return; +#ifdef DTRAIN_FASTER_PERCEPTRON + } +#endif } } } @@ -86,7 +119,7 @@ _PRO_cmp_pair_by_diff_d(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score)); } inline void -PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused=1) +PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, unsigned max, float _unused=1) { unsigned max_count = 5000, count = 0, sz = s->size(); bool b = false; diff --git a/dtrain/score.cc b/dtrain/score.cc index 7b1f6be4..4a7cac6e 100644 --- a/dtrain/score.cc +++ b/dtrain/score.cc @@ -80,7 +80,7 @@ StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, * to Machine Translation" * (Liang et al. '06) * - * NOTE: max is 0.9375 + * NOTE: max is 0.9375 (with N=4) */ score_t SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, @@ -103,7 +103,83 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, i_bleu[j] += (1/((score_t)j+1)) * i_ng; } } - sum += exp(i_bleu[i])/(pow(2.0, static_cast<double>(N_-i))); + sum += exp(i_bleu[i])/(pow(2.0, N_-i)); + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' bleu + * + * sum up Ngram precisions + */ +score_t +SumBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned /*rank*/, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (hyp_len == 0 || ref_len == 0) return 0.; + NgramCounts counts = make_ngram_counts(hyp, ref, N_); + unsigned M = N_; + if (ref_len < N_) M = ref_len; + score_t sum = 0.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += ((score_t)counts.clipped_[i]/counts.sum_[i])/pow(2., N_-j+1); + j++; + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' (exp) bleu + * + * sum up exp(Ngram precisions) + */ +score_t +SumExpBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned /*rank*/, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (hyp_len == 0 || ref_len == 0) return 0.; + NgramCounts counts = make_ngram_counts(hyp, ref, N_); + unsigned M = N_; + if (ref_len < N_) M = ref_len; + score_t sum = 0.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += exp(((score_t)counts.clipped_[i]/counts.sum_[i]))/pow(2., N_-j+1); + j++; + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * 'sum' (whatever) bleu + * + * sum up exp(weight * log(Ngram precisions)) + */ +score_t +SumWhateverBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned /*rank*/, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (hyp_len == 0 || ref_len == 0) return 0.; + NgramCounts counts = make_ngram_counts(hyp, ref, N_); + unsigned M = N_; + vector<score_t> v = w_; + if (ref_len < N_) { + M = ref_len; + for (unsigned i = 0; i < M; i++) v[i] = 1/((score_t)M); + } + score_t sum = 0.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += exp(v[i] * log(((score_t)counts.clipped_[i]/counts.sum_[i])))/pow(2., N_-j+1); + j++; } return brevity_penalty(hyp_len, ref_len) * sum; } @@ -115,7 +191,8 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, * and Structural Translation Features" * (Chiang et al. '08) * - * NOTE: needs some more code in dtrain.cc + * NOTE: Needs some more code in dtrain.cc . + * No scaling by src len. */ score_t ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, @@ -137,7 +214,39 @@ ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, glob_ref_len_ = discount_ * (glob_ref_len_ + ref_len); glob_src_len_ = discount_ * (glob_src_len_ + src_len); } - return (score_t)glob_src_len_ * score; + return score; +} + +/* + * Linear (Corpus) Bleu + * + * as in "Lattice Minimum Bayes-Risk Decoding + * for Statistical Machine Translation" + * (Tromble et al. '08) + * + */ +score_t +LinearBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned rank, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (ref_len == 0) return 0.; + unsigned M = N_; + if (ref_len < N_) M = ref_len; + NgramCounts counts(M); + if (hyp_len > 0) + counts = make_ngram_counts(hyp, ref, M); + score_t ret = 0.; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || onebest_counts_.sum_[i] == 0) break; + ret += counts.sum_[i]/onebest_counts_.sum_[i]; + } + ret = -(hyp_len/(score_t)onebest_len_) + (1./M) * ret; + if (rank == 0) { + onebest_len_ += hyp_len; + onebest_counts_ += counts; + } + return ret; } diff --git a/dtrain/score.h b/dtrain/score.h index eb8ad912..f317c903 100644 --- a/dtrain/score.h +++ b/dtrain/score.h @@ -20,7 +20,7 @@ struct NgramCounts inline void operator+=(const NgramCounts& rhs) { - assert(N_ == rhs.N_); + if (rhs.N_ > N_) Resize(rhs.N_); for (unsigned i = 0; i < N_; i++) { this->clipped_[i] += rhs.clipped_.find(i)->second; this->sum_[i] += rhs.sum_.find(i)->second; @@ -59,14 +59,22 @@ struct NgramCounts inline void Zero() { - unsigned i; - for (i = 0; i < N_; i++) { + for (unsigned i = 0; i < N_; i++) { clipped_[i] = 0.; sum_[i] = 0.; } } inline void + One() + { + for (unsigned i = 0; i < N_; i++) { + clipped_[i] = 1.; + sum_[i] = 1.; + } + } + + inline void Print() { for (unsigned i = 0; i < N_; i++) { @@ -74,6 +82,23 @@ struct NgramCounts cout << i+1 << "grams:\t\t\t" << sum_[i] << endl; } } + + inline void Resize(unsigned N) + { + if (N == N_) return; + else if (N > N_) { + for (unsigned i = N_; i < N; i++) { + clipped_[i] = 0.; + sum_[i] = 0.; + } + } else { // N < N_ + for (unsigned i = N_-1; i > N-1; i--) { + clipped_.erase(i); + sum_.erase(i); + } + } + N_ = N; + } }; typedef map<vector<WordID>, unsigned> Ngrams; @@ -128,6 +153,21 @@ struct SmoothBleuScorer : public LocalScorer score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); }; +struct SumBleuScorer : public LocalScorer +{ + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + +struct SumExpBleuScorer : public LocalScorer +{ + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + +struct SumWhateverBleuScorer : public LocalScorer +{ + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + struct ApproxBleuScorer : public BleuScorer { NgramCounts glob_onebest_counts_; @@ -147,6 +187,24 @@ struct ApproxBleuScorer : public BleuScorer score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len); }; +struct LinearBleuScorer : public BleuScorer +{ + unsigned onebest_len_; + NgramCounts onebest_counts_; + + LinearBleuScorer(unsigned N) : onebest_len_(1), onebest_counts_(N) + { + onebest_counts_.One(); + } + + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned /*src_len*/); + + inline void Reset() { + onebest_len_ = 1; + onebest_counts_.One(); + } +}; + } // namespace diff --git a/dtrain/test/example/README b/dtrain/test/example/README index b3ea5f06..6937b11b 100644 --- a/dtrain/test/example/README +++ b/dtrain/test/example/README @@ -1,8 +1,8 @@ Small example of input format for distributed training. Call dtrain from cdec/dtrain/ with ./dtrain -c test/example/dtrain.ini . -For this to work, disable '#define DTRAIN_LOCAL' from dtrain.h +For this to work, undef 'DTRAIN_LOCAL' in dtrain.h and recompile. -Data is here: http://simianer.de/dtrain +Data is here: http://simianer.de/#dtrain diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini index f87ee9cf..c8ac7c3f 100644 --- a/dtrain/test/example/dtrain.ini +++ b/dtrain/test/example/dtrain.ini @@ -5,7 +5,7 @@ decoder_config=test/example/cdec.ini # config for cdec # weights for these features will be printed on each iteration print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough tmp=/tmp -stop_after=10 # stop epoch after 20 inputs +stop_after=10 # stop epoch after 10 inputs # interesting stuff epochs=3 # run over input 3 times @@ -19,3 +19,4 @@ filter=uniq # only unique entries in kbest (surface form) pair_sampling=XYX hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here pair_threshold=0 # minimum distance in BLEU (this will still only use pairs with diff > 0) +loss_margin=0 diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output new file mode 100644 index 00000000..25d2c069 --- /dev/null +++ b/dtrain/test/example/expected-output @@ -0,0 +1,125 @@ + cdec cfg 'test/example/cdec.ini' +feature: WordPenalty (no config parameters) +State is 0 bytes for feature WordPenalty +feature: KLanguageModel (with config parameters 'test/example/nc-wmt11.en.srilm.gz') +Loading the LM will be faster if you build a binary file. +Reading test/example/nc-wmt11.en.srilm.gz +----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 +**************************************************************************************************** +Loaded 5-gram KLM from test/example/nc-wmt11.en.srilm.gz (MapSize=49581) +State is 98 bytes for feature KLanguageModel test/example/nc-wmt11.en.srilm.gz +feature: RuleIdentityFeatures (no config parameters) +State is 0 bytes for feature RuleIdentityFeatures +feature: RuleNgramFeatures (no config parameters) +State is 0 bytes for feature RuleNgramFeatures +feature: RuleShape (no config parameters) + Example feature: Shape_S00000_T00000 +State is 0 bytes for feature RuleShape +Seeding random number sequence to 1072059181 + +dtrain +Parameters: + k 100 + N 4 + T 3 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'VOID' + l1 reg 0 'none' + cdec cfg 'test/example/cdec.ini' + input 'test/example/nc-wmt11.1k.gz' + output '-' + stop_after 10 +(a dot represents 10 inputs) +Iteration #1 of 3. + . 10 +Stopping after 10 input sentences. +WEIGHTS + Glue = -0.0293 + WordPenalty = +0.049075 + LanguageModel = +0.24345 + LanguageModel_OOV = -0.2029 + PhraseModel_0 = +0.0084102 + PhraseModel_1 = +0.021729 + PhraseModel_2 = +0.014922 + PhraseModel_3 = +0.104 + PhraseModel_4 = -0.14308 + PhraseModel_5 = +0.0247 + PhraseModel_6 = -0.012 + PassThrough = -0.2161 + --- + 1best avg score: 0.16872 (+0.16872) + 1best avg model score: -1.8276 (-1.8276) + avg # pairs: 1121.1 + avg # rank err: 555.6 + avg # margin viol: 0 + non0 feature count: 277 + avg list sz: 77.2 + avg f count: 90.96 +(time 0.1 min, 0.6 s/S) + +Iteration #2 of 3. + . 10 +WEIGHTS + Glue = -0.3526 + WordPenalty = +0.067576 + LanguageModel = +1.155 + LanguageModel_OOV = -0.2728 + PhraseModel_0 = -0.025529 + PhraseModel_1 = +0.095869 + PhraseModel_2 = +0.094567 + PhraseModel_3 = +0.12482 + PhraseModel_4 = -0.36533 + PhraseModel_5 = +0.1068 + PhraseModel_6 = -0.1517 + PassThrough = -0.286 + --- + 1best avg score: 0.18394 (+0.015221) + 1best avg model score: 3.205 (+5.0326) + avg # pairs: 1168.3 + avg # rank err: 594.8 + avg # margin viol: 0 + non0 feature count: 543 + avg list sz: 77.5 + avg f count: 85.916 +(time 0.083 min, 0.5 s/S) + +Iteration #3 of 3. + . 10 +WEIGHTS + Glue = -0.392 + WordPenalty = +0.071963 + LanguageModel = +0.81266 + LanguageModel_OOV = -0.4177 + PhraseModel_0 = -0.2649 + PhraseModel_1 = -0.17931 + PhraseModel_2 = +0.038261 + PhraseModel_3 = +0.20261 + PhraseModel_4 = -0.42621 + PhraseModel_5 = +0.3198 + PhraseModel_6 = -0.1437 + PassThrough = -0.4309 + --- + 1best avg score: 0.2962 (+0.11225) + 1best avg model score: -36.274 (-39.479) + avg # pairs: 1109.6 + avg # rank err: 515.9 + avg # margin viol: 0 + non0 feature count: 741 + avg list sz: 77 + avg f count: 88.982 +(time 0.083 min, 0.5 s/S) + +Writing weights file to '-' ... +done + +--- +Best iteration: 3 [SCORE 'stupid_bleu'=0.2962]. +This took 0.26667 min. |