From 9ae3a1b4de9e65a9f9bc9bf322bf5a04cb7a81ea Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Thu, 25 Apr 2013 10:16:39 +0200 Subject: parallellized example --- training/dtrain/examples/parallelized/cdec.ini | 2 +- training/dtrain/examples/parallelized/work/out.0.0 | 9 +++++---- training/dtrain/examples/parallelized/work/out.0.1 | 9 +++++---- training/dtrain/examples/parallelized/work/out.1.0 | 9 +++++---- training/dtrain/examples/parallelized/work/out.1.1 | 9 +++++---- 5 files changed, 21 insertions(+), 17 deletions(-) (limited to 'training/dtrain/examples') diff --git a/training/dtrain/examples/parallelized/cdec.ini b/training/dtrain/examples/parallelized/cdec.ini index e43ba1c4..5773029a 100644 --- a/training/dtrain/examples/parallelized/cdec.ini +++ b/training/dtrain/examples/parallelized/cdec.ini @@ -4,7 +4,7 @@ intersection_strategy=cube_pruning cubepruning_pop_limit=200 scfg_max_span_limit=15 feature_function=WordPenalty -feature_function=KLanguageModel ../example/nc-wmt11.en.srilm.gz +feature_function=KLanguageModel ../standard//nc-wmt11.en.srilm.gz #feature_function=ArityPenalty #feature_function=CMR2008ReorderingFeatures #feature_function=Dwarf diff --git a/training/dtrain/examples/parallelized/work/out.0.0 b/training/dtrain/examples/parallelized/work/out.0.0 index 7a00ed0f..c559dd4d 100644 --- a/training/dtrain/examples/parallelized/work/out.0.0 +++ b/training/dtrain/examples/parallelized/work/out.0.0 @@ -1,9 +1,9 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../example/nc-wmt11.en.srilm.gz +Reading ../standard//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 **************************************************************************************************** -Seeding random number sequence to 3121929377 +Seeding random number sequence to 405292278 dtrain Parameters: @@ -16,6 +16,7 @@ Parameters: learning rate 0.0001 gamma 0 loss margin 1 + faster perceptron 0 pairs 'XYX' hi lo 0.1 pair threshold 0 @@ -51,11 +52,11 @@ WEIGHTS non0 feature count: 12 avg list sz: 100 avg f count: 11.32 -(time 0.37 min, 4.4 s/S) +(time 0.35 min, 4.2 s/S) Writing weights file to 'work/weights.0.0' ... done --- Best iteration: 1 [SCORE 'stupid_bleu'=0.17521]. -This took 0.36667 min. +This took 0.35 min. diff --git a/training/dtrain/examples/parallelized/work/out.0.1 b/training/dtrain/examples/parallelized/work/out.0.1 index e2bd6649..8bc7ea9c 100644 --- a/training/dtrain/examples/parallelized/work/out.0.1 +++ b/training/dtrain/examples/parallelized/work/out.0.1 @@ -1,9 +1,9 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../example/nc-wmt11.en.srilm.gz +Reading ../standard//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 **************************************************************************************************** -Seeding random number sequence to 2767202922 +Seeding random number sequence to 43859692 dtrain Parameters: @@ -16,6 +16,7 @@ Parameters: learning rate 0.0001 gamma 0 loss margin 1 + faster perceptron 0 pairs 'XYX' hi lo 0.1 pair threshold 0 @@ -52,11 +53,11 @@ WEIGHTS non0 feature count: 12 avg list sz: 100 avg f count: 10.496 -(time 0.32 min, 3.8 s/S) +(time 0.35 min, 4.2 s/S) Writing weights file to 'work/weights.0.1' ... done --- Best iteration: 1 [SCORE 'stupid_bleu'=0.26638]. -This took 0.31667 min. +This took 0.35 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.0 b/training/dtrain/examples/parallelized/work/out.1.0 index 6e790e38..65d1e7dc 100644 --- a/training/dtrain/examples/parallelized/work/out.1.0 +++ b/training/dtrain/examples/parallelized/work/out.1.0 @@ -1,9 +1,9 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../example/nc-wmt11.en.srilm.gz +Reading ../standard//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 **************************************************************************************************** -Seeding random number sequence to 1432415010 +Seeding random number sequence to 4126799437 dtrain Parameters: @@ -16,6 +16,7 @@ Parameters: learning rate 0.0001 gamma 0 loss margin 1 + faster perceptron 0 pairs 'XYX' hi lo 0.1 pair threshold 0 @@ -51,11 +52,11 @@ WEIGHTS non0 feature count: 11 avg list sz: 100 avg f count: 11.814 -(time 0.45 min, 5.4 s/S) +(time 0.43 min, 5.2 s/S) Writing weights file to 'work/weights.1.0' ... done --- Best iteration: 1 [SCORE 'stupid_bleu'=0.10863]. -This took 0.45 min. +This took 0.43333 min. diff --git a/training/dtrain/examples/parallelized/work/out.1.1 b/training/dtrain/examples/parallelized/work/out.1.1 index 0b984761..f479fbbc 100644 --- a/training/dtrain/examples/parallelized/work/out.1.1 +++ b/training/dtrain/examples/parallelized/work/out.1.1 @@ -1,9 +1,9 @@ cdec cfg 'cdec.ini' Loading the LM will be faster if you build a binary file. -Reading ../example/nc-wmt11.en.srilm.gz +Reading ../standard//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 **************************************************************************************************** -Seeding random number sequence to 1771918374 +Seeding random number sequence to 2112412848 dtrain Parameters: @@ -16,6 +16,7 @@ Parameters: learning rate 0.0001 gamma 0 loss margin 1 + faster perceptron 0 pairs 'XYX' hi lo 0.1 pair threshold 0 @@ -52,11 +53,11 @@ WEIGHTS non0 feature count: 12 avg list sz: 100 avg f count: 11.224 -(time 0.42 min, 5 s/S) +(time 0.45 min, 5.4 s/S) Writing weights file to 'work/weights.1.1' ... done --- Best iteration: 1 [SCORE 'stupid_bleu'=0.13169]. -This took 0.41667 min. +This took 0.45 min. -- cgit v1.2.3 From d18024a41cbc1b54db88d499571349a6234b6db8 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Mon, 29 Apr 2013 15:24:39 +0200 Subject: fix, cleaned up headers --- training/dtrain/dtrain.cc | 28 +++++--- training/dtrain/dtrain.h | 74 ++++++++++++++++---- training/dtrain/examples/standard/dtrain.ini | 24 +++---- training/dtrain/examples/standard/expected-output | 84 +++++++++++------------ training/dtrain/kbestget.h | 66 +----------------- training/dtrain/ksampler.h | 5 +- training/dtrain/score.h | 17 +++-- 7 files changed, 144 insertions(+), 154 deletions(-) (limited to 'training/dtrain/examples') diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc index 149f87d4..83e4e440 100644 --- a/training/dtrain/dtrain.cc +++ b/training/dtrain/dtrain.cc @@ -1,4 +1,10 @@ #include "dtrain.h" +#include "score.h" +#include "kbestget.h" +#include "ksampler.h" +#include "pairsampling.h" + +using namespace dtrain; bool @@ -138,23 +144,23 @@ main(int argc, char** argv) string scorer_str = cfg["scorer"].as(); LocalScorer* scorer; if (scorer_str == "bleu") { - scorer = dynamic_cast(new BleuScorer); + scorer = static_cast(new BleuScorer); } else if (scorer_str == "stupid_bleu") { - scorer = dynamic_cast(new StupidBleuScorer); + scorer = static_cast(new StupidBleuScorer); } else if (scorer_str == "fixed_stupid_bleu") { - scorer = dynamic_cast(new FixedStupidBleuScorer); + scorer = static_cast(new FixedStupidBleuScorer); } else if (scorer_str == "smooth_bleu") { - scorer = dynamic_cast(new SmoothBleuScorer); + scorer = static_cast(new SmoothBleuScorer); } else if (scorer_str == "sum_bleu") { - scorer = dynamic_cast(new SumBleuScorer); + scorer = static_cast(new SumBleuScorer); } else if (scorer_str == "sumexp_bleu") { - scorer = dynamic_cast(new SumExpBleuScorer); + scorer = static_cast(new SumExpBleuScorer); } else if (scorer_str == "sumwhatever_bleu") { - scorer = dynamic_cast(new SumWhateverBleuScorer); + scorer = static_cast(new SumWhateverBleuScorer); } else if (scorer_str == "approx_bleu") { - scorer = dynamic_cast(new ApproxBleuScorer(N, approx_bleu_d)); + scorer = static_cast(new ApproxBleuScorer(N, approx_bleu_d)); } else if (scorer_str == "lc_bleu") { - scorer = dynamic_cast(new LinearBleuScorer(N)); + scorer = static_cast(new LinearBleuScorer(N)); } else { cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl; exit(1); @@ -166,9 +172,9 @@ main(int argc, char** argv) MT19937 rng; // random number generator, only for forest sampling HypSampler* observer; if (sample_from == "kbest") - observer = dynamic_cast(new KBestGetter(k, filter_type)); + observer = static_cast(new KBestGetter(k, filter_type)); else - observer = dynamic_cast(new KSampler(k, &rng)); + observer = static_cast(new KSampler(k, &rng)); observer->SetScorer(scorer); // init weights diff --git a/training/dtrain/dtrain.h b/training/dtrain/dtrain.h index eb0b9f17..3981fb39 100644 --- a/training/dtrain/dtrain.h +++ b/training/dtrain/dtrain.h @@ -11,16 +11,19 @@ #include #include -#include "ksampler.h" -#include "pairsampling.h" - -#include "filelib.h" - +#include "decoder.h" +#include "ff_register.h" +#include "sentence_metadata.h" +#include "verbose.h" +#include "viterbi.h" using namespace std; -using namespace dtrain; namespace po = boost::program_options; +namespace dtrain +{ + + inline void register_and_convert(const vector& strs, vector& ids) { vector::const_iterator it; @@ -42,17 +45,55 @@ inline string gettmpf(const string path, const string infix) return string(fn); } -inline void split_in(string& s, vector& parts) +typedef double score_t; + +struct ScoredHyp { - unsigned f = 0; - for(unsigned i = 0; i < 3; i++) { - unsigned e = f; - f = s.find("\t", f+1); - if (e != 0) parts.push_back(s.substr(e+1, f-e-1)); - else parts.push_back(s.substr(0, f)); + vector w; + SparseVector f; + score_t model; + score_t score; + unsigned rank; +}; + +struct LocalScorer +{ + unsigned N_; + vector w_; + + virtual score_t + Score(vector& hyp, vector& ref, const unsigned rank, const unsigned src_len)=0; + + virtual void Reset() {} // only for ApproxBleuScorer, LinearBleuScorer + + inline void + Init(unsigned N, vector weights) + { + assert(N > 0); + N_ = N; + if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_); + else w_ = weights; } - s.erase(0, f+1); -} + + inline score_t + brevity_penalty(const unsigned hyp_len, const unsigned ref_len) + { + if (hyp_len > ref_len) return 1; + return exp(1 - (score_t)ref_len/hyp_len); + } +}; + +struct HypSampler : public DecoderObserver +{ + LocalScorer* scorer_; + vector* ref_; + unsigned f_count_, sz_; + virtual vector* GetSamples()=0; + inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; } + inline void SetRef(vector& ref) { ref_ = &ref; } + inline unsigned get_f_count() { return f_count_; } + inline unsigned get_sz() { return sz_; } +}; struct HSReporter { @@ -88,5 +129,8 @@ inline T sign(T z) return z < 0 ? -1 : +1; } + +} // namespace + #endif diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini index e1072d30..23e94285 100644 --- a/training/dtrain/examples/standard/dtrain.ini +++ b/training/dtrain/examples/standard/dtrain.ini @@ -10,15 +10,15 @@ print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 Phr stop_after=10 # stop epoch after 10 inputs # interesting stuff -epochs=2 # run over input 2 times -k=100 # use 100best lists -N=4 # optimize (approx) BLEU4 -scorer=stupid_bleu # use 'stupid' BLEU+1 -learning_rate=1.0 # learning rate, don't care if gamma=0 (perceptron) -gamma=0 # use SVM reg -sample_from=kbest # use kbest lists (as opposed to forest) -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 (here: > 0) -loss_margin=0 # update if correctly ranked, but within this margin +epochs=2 # run over input 2 times +k=100 # use 100best lists +N=4 # optimize (approx) BLEU4 +scorer=fixed_stupid_bleu # use 'stupid' BLEU+1 +learning_rate=1.0 # learning rate, don't care if gamma=0 (perceptron) +gamma=0 # use SVM reg +sample_from=kbest # use kbest lists (as opposed to forest) +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 (here: > 0) +loss_margin=0 # update if correctly ranked, but within this margin diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output index 7cd09dbf..9a25062b 100644 --- a/training/dtrain/examples/standard/expected-output +++ b/training/dtrain/examples/standard/expected-output @@ -4,14 +4,14 @@ Reading ./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 **************************************************************************************************** Example feature: Shape_S00000_T00000 -Seeding random number sequence to 2679584485 +Seeding random number sequence to 1677737427 dtrain Parameters: k 100 N 4 T 2 - scorer 'stupid_bleu' + scorer 'fixed_stupid_bleu' sample from 'kbest' filter 'uniq' learning rate 1 @@ -34,58 +34,58 @@ Iteration #1 of 2. . 10 Stopping after 10 input sentences. WEIGHTS - Glue = -576 - WordPenalty = +417.79 - LanguageModel = +5117.5 - LanguageModel_OOV = -1307 - PhraseModel_0 = -1612 - PhraseModel_1 = -2159.6 - PhraseModel_2 = -677.36 - PhraseModel_3 = +2663.8 - PhraseModel_4 = -1025.9 - PhraseModel_5 = -8 - PhraseModel_6 = +70 - PassThrough = -1455 + Glue = -1155 + WordPenalty = -329.63 + LanguageModel = +3903 + LanguageModel_OOV = -1630 + PhraseModel_0 = +2746.9 + PhraseModel_1 = +1200.3 + PhraseModel_2 = -1004.1 + PhraseModel_3 = +2223.1 + PhraseModel_4 = +551.58 + PhraseModel_5 = +217 + PhraseModel_6 = +1816 + PassThrough = -1603 --- - 1best avg score: 0.27697 (+0.27697) - 1best avg model score: -47918 (-47918) - avg # pairs: 581.9 (meaningless) - avg # rank err: 581.9 + 1best avg score: 0.19344 (+0.19344) + 1best avg model score: 81387 (+81387) + avg # pairs: 616.3 (meaningless) + avg # rank err: 616.3 avg # margin viol: 0 - non0 feature count: 703 + non0 feature count: 673 avg list sz: 90.9 - avg f count: 100.09 -(time 0.25 min, 1.5 s/S) + avg f count: 104.26 +(time 0.38 min, 2.3 s/S) Iteration #2 of 2. . 10 WEIGHTS - Glue = -622 - WordPenalty = +898.56 - LanguageModel = +8066.2 - LanguageModel_OOV = -2590 - PhraseModel_0 = -4335.8 - PhraseModel_1 = -5864.4 - PhraseModel_2 = -1729.8 - PhraseModel_3 = +2831.9 - PhraseModel_4 = -5384.8 - PhraseModel_5 = +1449 - PhraseModel_6 = +480 - PassThrough = -2578 + Glue = -994 + WordPenalty = -778.69 + LanguageModel = +2348.9 + LanguageModel_OOV = -1967 + PhraseModel_0 = -412.72 + PhraseModel_1 = +1428.9 + PhraseModel_2 = +1967.4 + PhraseModel_3 = -944.99 + PhraseModel_4 = -239.7 + PhraseModel_5 = +708 + PhraseModel_6 = +645 + PassThrough = -1866 --- - 1best avg score: 0.37119 (+0.094226) - 1best avg model score: -1.3174e+05 (-83822) - avg # pairs: 584.1 (meaningless) - avg # rank err: 584.1 + 1best avg score: 0.22395 (+0.03051) + 1best avg model score: -31388 (-1.1278e+05) + avg # pairs: 702.3 (meaningless) + avg # rank err: 702.3 avg # margin viol: 0 - non0 feature count: 1115 + non0 feature count: 955 avg list sz: 91.3 - avg f count: 90.755 -(time 0.3 min, 1.8 s/S) + avg f count: 103.45 +(time 0.32 min, 1.9 s/S) Writing weights file to '-' ... done --- -Best iteration: 2 [SCORE 'stupid_bleu'=0.37119]. -This took 0.55 min. +Best iteration: 2 [SCORE 'fixed_stupid_bleu'=0.22395]. +This took 0.7 min. diff --git a/training/dtrain/kbestget.h b/training/dtrain/kbestget.h index dd8882e1..85252db3 100644 --- a/training/dtrain/kbestget.h +++ b/training/dtrain/kbestget.h @@ -1,76 +1,12 @@ #ifndef _DTRAIN_KBESTGET_H_ #define _DTRAIN_KBESTGET_H_ -#include "kbest.h" // cdec -#include "sentence_metadata.h" - -#include "verbose.h" -#include "viterbi.h" -#include "ff_register.h" -#include "decoder.h" -#include "weights.h" -#include "logval.h" - -using namespace std; +#include "kbest.h" namespace dtrain { -typedef double score_t; - -struct ScoredHyp -{ - vector w; - SparseVector f; - score_t model; - score_t score; - unsigned rank; -}; - -struct LocalScorer -{ - unsigned N_; - vector w_; - - virtual score_t - Score(vector& hyp, vector& ref, const unsigned rank, const unsigned src_len)=0; - - void Reset() {} // only for approx bleu - - inline void - Init(unsigned N, vector weights) - { - assert(N > 0); - N_ = N; - if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_); - else w_ = weights; - } - - inline score_t - brevity_penalty(const unsigned hyp_len, const unsigned ref_len) - { - if (hyp_len > ref_len) return 1; - return exp(1 - (score_t)ref_len/hyp_len); - } -}; - -struct HypSampler : public DecoderObserver -{ - LocalScorer* scorer_; - vector* ref_; - unsigned f_count_, sz_; - virtual vector* GetSamples()=0; - inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; } - inline void SetRef(vector& ref) { ref_ = &ref; } - inline unsigned get_f_count() { return f_count_; } - inline unsigned get_sz() { return sz_; } -}; -//////////////////////////////////////////////////////////////////////////////// - - - - struct KBestGetter : public HypSampler { const unsigned k_; diff --git a/training/dtrain/ksampler.h b/training/dtrain/ksampler.h index bc2f56cd..29dab667 100644 --- a/training/dtrain/ksampler.h +++ b/training/dtrain/ksampler.h @@ -1,13 +1,12 @@ #ifndef _DTRAIN_KSAMPLER_H_ #define _DTRAIN_KSAMPLER_H_ -#include "hg_sampler.h" // cdec -#include "kbestget.h" -#include "score.h" +#include "hg_sampler.h" namespace dtrain { + bool cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b) { diff --git a/training/dtrain/score.h b/training/dtrain/score.h index bddaa071..53e970ba 100644 --- a/training/dtrain/score.h +++ b/training/dtrain/score.h @@ -1,9 +1,7 @@ #ifndef _DTRAIN_SCORE_H_ #define _DTRAIN_SCORE_H_ -#include "kbestget.h" - -using namespace std; +#include "dtrain.h" namespace dtrain { @@ -141,36 +139,43 @@ struct BleuScorer : public LocalScorer { score_t Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len); score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {} }; struct StupidBleuScorer : public LocalScorer { score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {} }; struct FixedStupidBleuScorer : public LocalScorer { score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {} }; struct SmoothBleuScorer : public LocalScorer { score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {} }; struct SumBleuScorer : public LocalScorer { - score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {} }; struct SumExpBleuScorer : public LocalScorer { - score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {} }; struct SumWhateverBleuScorer : public LocalScorer { - score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + score_t Score(vector& hyp, vector& ref, const unsigned /*rank*/, const unsigned /*src_len*/); + void Reset() {}; }; struct ApproxBleuScorer : public BleuScorer -- cgit v1.2.3 From a2b3a566019f5e8b1fd6a7893600c082b99464e5 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Thu, 2 May 2013 10:04:07 +0200 Subject: updated example --- training/dtrain/examples/standard/expected-output | 82 +++++++++++------------ 1 file changed, 41 insertions(+), 41 deletions(-) (limited to 'training/dtrain/examples') diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output index 9a25062b..21f91244 100644 --- a/training/dtrain/examples/standard/expected-output +++ b/training/dtrain/examples/standard/expected-output @@ -4,7 +4,7 @@ Reading ./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 **************************************************************************************************** Example feature: Shape_S00000_T00000 -Seeding random number sequence to 1677737427 +Seeding random number sequence to 970626287 dtrain Parameters: @@ -34,58 +34,58 @@ Iteration #1 of 2. . 10 Stopping after 10 input sentences. WEIGHTS - Glue = -1155 - WordPenalty = -329.63 - LanguageModel = +3903 - LanguageModel_OOV = -1630 - PhraseModel_0 = +2746.9 - PhraseModel_1 = +1200.3 - PhraseModel_2 = -1004.1 - PhraseModel_3 = +2223.1 - PhraseModel_4 = +551.58 - PhraseModel_5 = +217 - PhraseModel_6 = +1816 - PassThrough = -1603 + Glue = -614 + WordPenalty = +1256.8 + LanguageModel = +5610.5 + LanguageModel_OOV = -1449 + PhraseModel_0 = -2107 + PhraseModel_1 = -4666.1 + PhraseModel_2 = -2713.5 + PhraseModel_3 = +4204.3 + PhraseModel_4 = -1435.8 + PhraseModel_5 = +916 + PhraseModel_6 = +190 + PassThrough = -2527 --- - 1best avg score: 0.19344 (+0.19344) - 1best avg model score: 81387 (+81387) - avg # pairs: 616.3 (meaningless) - avg # rank err: 616.3 + 1best avg score: 0.17874 (+0.17874) + 1best avg model score: 88399 (+88399) + avg # pairs: 798.2 (meaningless) + avg # rank err: 798.2 avg # margin viol: 0 - non0 feature count: 673 - avg list sz: 90.9 - avg f count: 104.26 -(time 0.38 min, 2.3 s/S) + non0 feature count: 887 + avg list sz: 91.3 + avg f count: 126.85 +(time 0.33 min, 2 s/S) Iteration #2 of 2. . 10 WEIGHTS - Glue = -994 - WordPenalty = -778.69 - LanguageModel = +2348.9 - LanguageModel_OOV = -1967 - PhraseModel_0 = -412.72 - PhraseModel_1 = +1428.9 - PhraseModel_2 = +1967.4 - PhraseModel_3 = -944.99 - PhraseModel_4 = -239.7 - PhraseModel_5 = +708 - PhraseModel_6 = +645 - PassThrough = -1866 + Glue = -1025 + WordPenalty = +1751.5 + LanguageModel = +10059 + LanguageModel_OOV = -4490 + PhraseModel_0 = -2640.7 + PhraseModel_1 = -3757.4 + PhraseModel_2 = -1133.1 + PhraseModel_3 = +1837.3 + PhraseModel_4 = -3534.3 + PhraseModel_5 = +2308 + PhraseModel_6 = +1677 + PassThrough = -6222 --- - 1best avg score: 0.22395 (+0.03051) - 1best avg model score: -31388 (-1.1278e+05) - avg # pairs: 702.3 (meaningless) - avg # rank err: 702.3 + 1best avg score: 0.30764 (+0.12891) + 1best avg model score: -2.5042e+05 (-3.3882e+05) + avg # pairs: 725.9 (meaningless) + avg # rank err: 725.9 avg # margin viol: 0 - non0 feature count: 955 + non0 feature count: 1499 avg list sz: 91.3 - avg f count: 103.45 + avg f count: 114.34 (time 0.32 min, 1.9 s/S) Writing weights file to '-' ... done --- -Best iteration: 2 [SCORE 'fixed_stupid_bleu'=0.22395]. -This took 0.7 min. +Best iteration: 2 [SCORE 'fixed_stupid_bleu'=0.30764]. +This took 0.65 min. -- cgit v1.2.3 From b7ce1921924770754fd59d23118d89d8c34800c8 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Mon, 24 Jun 2013 17:45:32 +0200 Subject: documentation --- training/dtrain/README.md | 11 +++++++++++ training/dtrain/examples/parallelized/dtrain.ini | 2 -- 2 files changed, 11 insertions(+), 2 deletions(-) (limited to 'training/dtrain/examples') diff --git a/training/dtrain/README.md b/training/dtrain/README.md index 2ab2f232..2bae6b48 100644 --- a/training/dtrain/README.md +++ b/training/dtrain/README.md @@ -17,6 +17,17 @@ To build only parts needed for dtrain do cd training/dtrain/; make ``` +Ideas +----- + * get approx_bleu to work? + * implement minibatches (Minibatch and Parallelization for Online Large Margin Structured Learning) + * learning rate 1/T? + * use an oracle? mira-like (model vs. BLEU), feature repr. of reference!? + * implement lc_bleu properly + * merge kbest lists of previous epochs (as MERT does) + * ``walk entire regularization path'' + * rerank after each update? + Running ------- See directories under test/ . diff --git a/training/dtrain/examples/parallelized/dtrain.ini b/training/dtrain/examples/parallelized/dtrain.ini index f19ef891..0b0932d6 100644 --- a/training/dtrain/examples/parallelized/dtrain.ini +++ b/training/dtrain/examples/parallelized/dtrain.ini @@ -11,6 +11,4 @@ pair_sampling=XYX hi_lo=0.1 select_weights=last print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough -# newer version of the grammar extractor use different feature names: -#print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough decoder_config=cdec.ini -- cgit v1.2.3