diff options
Diffstat (limited to 'training/mira/kbest_cut_mira.cc')
| -rw-r--r-- | training/mira/kbest_cut_mira.cc | 1010 |
1 files changed, 1010 insertions, 0 deletions
diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc new file mode 100644 index 00000000..34eb00dc --- /dev/null +++ b/training/mira/kbest_cut_mira.cc @@ -0,0 +1,1010 @@ +#include <sstream> +#include <iostream> +#include <vector> +#include <cassert> +#include <cmath> +#include <algorithm> + +#include "config.h" + + +#include <boost/shared_ptr.hpp> +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "sentence_metadata.h" +#include "scorer.h" +#include "verbose.h" +#include "viterbi.h" +#include "hg.h" +#include "prob.h" +#include "kbest.h" +#include "ff_register.h" +#include "decoder.h" +#include "filelib.h" +#include "fdict.h" +#include "time.h" +#include "sampler.h" + +#include "weights.h" +#include "sparse_vector.h" + +using namespace std; +using boost::shared_ptr; +namespace po = boost::program_options; + +bool invert_score; +boost::shared_ptr<MT19937> rng; +bool approx_score; +bool no_reweight; +bool no_select; +bool unique_kbest; +int update_list_size; +vector<weight_t> dense_weights_g; +double mt_metric_scale; +int optimizer; +int fear_select; +int hope_select; +bool pseudo_doc; +bool sent_approx; +bool checkloss; + +void SanityCheck(const vector<double>& w) { + for (int i = 0; i < w.size(); ++i) { + assert(!isnan(w[i])); + assert(!isinf(w[i])); + } +} + +struct FComp { + const vector<double>& w_; + FComp(const vector<double>& w) : w_(w) {} + bool operator()(int a, int b) const { + return fabs(w_[a]) > fabs(w_[b]); + } +}; + +void ShowLargestFeatures(const vector<double>& w) { + vector<int> fnums(w.size()); + for (int i = 0; i < w.size(); ++i) + fnums[i] = i; + vector<int>::iterator mid = fnums.begin(); + mid += (w.size() > 10 ? 10 : w.size()); + partial_sort(fnums.begin(), mid, fnums.end(), FComp(w)); + cerr << "TOP FEATURES:"; + for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) { + cerr << ' ' << FD::Convert(*i) << '=' << w[*i]; + } + cerr << endl; +} + +bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("input_weights,w",po::value<string>(),"Input feature weights file") + ("source,i",po::value<string>(),"Source file for development set") + ("pass,p", po::value<int>()->default_value(15), "Current pass through the training data") + ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)") + ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)") + ("optimizer,o",po::value<int>()->default_value(1), "Optimizer (SGD=1, PA MIRA w/Delta=2, Cutting Plane MIRA=3, PA MIRA=4, Triple nbest list MIRA=5)") + ("fear,f",po::value<int>()->default_value(1), "Fear selection (model-cost=1, maxcost=2, maxscore=3)") + ("hope,h",po::value<int>()->default_value(1), "Hope selection (model+cost=1, mincost=2)") + ("max_step_size,C", po::value<double>()->default_value(0.01), "regularization strength (C)") + ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)") + ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by") + ("sent_approx,a", "Use smoothed sentence-level BLEU score for approximate scoring") + ("pseudo_doc,e", "Use pseudo-document BLEU score for approximate scoring") + ("no_reweight,d","Do not reweight forest for cutting plane") + ("no_select,n", "Do not use selection heuristic") + ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles") + ("update_k_best,b", po::value<int>()->default_value(1), "Size of good, bad lists to perform update with") + ("unique_k_best,u", "Unique k-best translation list") + ("weights_output,O",po::value<string>(),"Directory to write weights to") + ("output_dir,D",po::value<string>(),"Directory to place output in") + ("decoder_config,c",po::value<string>(),"Decoder configuration file"); + po::options_description clo("Command line options"); + clo.add_options() + ("config", po::value<string>(), "Configuration file") + ("help,H", "Print this help message and exit"); + po::options_description dconfig_options, dcmdline_options; + dconfig_options.add(opts); + dcmdline_options.add(opts).add(clo); + + po::store(parse_command_line(argc, argv, dcmdline_options), *conf); + if (conf->count("config")) { + ifstream config((*conf)["config"].as<string>().c_str()); + po::store(po::parse_config_file(config, dconfig_options), *conf); + } + po::notify(*conf); + + if (conf->count("help") || !conf->count("input_weights") || !conf->count("decoder_config") || !conf->count("reference")) { + cerr << dcmdline_options << endl; + return false; + } + return true; +} + +//load previous translation, store array of each sentences score, subtract it from current sentence and replace with new translation score + + +static const double kMINUS_EPSILON = -1e-6; +static const double EPSILON = 0.000001; +static const double SMO_EPSILON = 0.0001; +static const double PSEUDO_SCALE = 0.95; +static const int MAX_SMO = 10; +int cur_pass; + +struct HypothesisInfo { + SparseVector<double> features; + vector<WordID> hyp; + double mt_metric; + double hope; + double fear; + double alpha; + double oracle_loss; + SparseVector<double> oracle_feat_diff; + shared_ptr<HypothesisInfo> oracleN; +}; + +bool ApproxEqual(double a, double b) { + if (a == b) return true; + return (fabs(a-b)/fabs(b)) < EPSILON; +} + +typedef shared_ptr<HypothesisInfo> HI; +bool HypothesisCompareB(const HI& h1, const HI& h2 ) +{ + return h1->mt_metric > h2->mt_metric; +}; + + +bool HopeCompareB(const HI& h1, const HI& h2 ) +{ + return h1->hope > h2->hope; +}; + +bool FearCompareB(const HI& h1, const HI& h2 ) +{ + return h1->fear > h2->fear; +}; + +bool FearComparePred(const HI& h1, const HI& h2 ) +{ + return h1->features.dot(dense_weights_g) > h2->features.dot(dense_weights_g); +}; + +bool HypothesisCompareG(const HI& h1, const HI& h2 ) +{ + return h1->mt_metric < h2->mt_metric; +}; + + +void CuttingPlane(vector<shared_ptr<HypothesisInfo> >* cur_c, bool* again, vector<shared_ptr<HypothesisInfo> >& all_hyp, vector<weight_t> dense_weights) +{ + bool DEBUG_CUT = false; + shared_ptr<HypothesisInfo> max_fear, max_fear_in_set; + vector<shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c; + + if(no_reweight) + { + //find new hope hypothesis + for(int u=0;u!=all_hyp.size();u++) + { + double t_score = all_hyp[u]->features.dot(dense_weights); + all_hyp[u]->hope = 1 * all_hyp[u]->mt_metric + t_score; + } + + //sort hyps by hope score + sort(all_hyp.begin(),all_hyp.end(),HopeCompareB); + + double hope_score = all_hyp[0]->features.dot(dense_weights); + if(DEBUG_CUT) cerr << "New hope derivation score " << hope_score << endl; + + for(int u=0;u!=all_hyp.size();u++) + { + double t_score = all_hyp[u]->features.dot(dense_weights); + //all_hyp[u]->fear = -1*all_hyp[u]->mt_metric - hope_score + t_score; + + all_hyp[u]->fear = -1*all_hyp[u]->mt_metric + 1*all_hyp[0]->mt_metric - hope_score + t_score; //relative loss + // all_hyp[u]->oracle_loss = -1*all_hyp[u]->mt_metric - -1*all_hyp[0]->mt_metric; + //all_hyp[u]->oracle_feat_diff = all_hyp[0]->features - all_hyp[u]->features; + // all_hyp[u]->fear = -1 * all_hyp[u]->mt_metric + t_score; + } + + sort(all_hyp.begin(),all_hyp.end(),FearCompareB); + + } + //assign maximum fear derivation from all derivations + max_fear = all_hyp[0]; + + if(DEBUG_CUT) cerr <<"Cutting Plane Max Fear "<<max_fear->fear ; + for(int i=0; i < cur_constraint.size();i++) //select maximal violator already in constraint set + { + if (!max_fear_in_set || cur_constraint[i]->fear > max_fear_in_set->fear) + max_fear_in_set = cur_constraint[i]; + } + if(DEBUG_CUT) cerr << "Max Fear in constraint set " << max_fear_in_set->fear << endl; + + if(max_fear->fear > max_fear_in_set->fear + SMO_EPSILON) + { + cur_constraint.push_back(max_fear); + *again = true; + if(DEBUG_CUT) cerr << "Optimize Again " << *again << endl; + } +} + + +double ComputeDelta(vector<shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights ) +{ + vector<shared_ptr<HypothesisInfo> >& cur_pair = *cur_p; + double loss = cur_pair[0]->oracle_loss - cur_pair[1]->oracle_loss; + //double margin = -cur_pair[0]->oracle_feat_diff.dot(dense_weights) + cur_pair[1]->oracle_feat_diff.dot(dense_weights); //TODO: is it a problem that new oracle is used in diff? + //double num = loss - margin; + + + double margin = -(cur_pair[0]->oracleN->features.dot(dense_weights)- cur_pair[0]->features.dot(dense_weights)) + (cur_pair[1]->oracleN->features.dot(dense_weights) - cur_pair[1]->features.dot(dense_weights)); + const double num = margin + loss; + cerr << "LOSS: " << num << " Margin:" << margin << " BLEUL:" << loss << " " << cur_pair[1]->features.dot(dense_weights) << " " << cur_pair[0]->features.dot(dense_weights) <<endl; + + +/* double num = + (cur_pair[0]->oracle_loss - cur_pair[0]->oracle_feat_diff.dot(dense_weights)) + - (cur_pair[1]->oracle_loss - cur_pair[1]->oracle_feat_diff.dot(dense_weights)); + */ + + SparseVector<double> diff = cur_pair[0]->features; + diff -= cur_pair[1]->features; + /* SparseVector<double> diff = cur_pair[0]->oracle_feat_diff; + diff -= cur_pair[1]->oracle_feat_diff;*/ + double diffsqnorm = diff.l2norm_sq(); + double delta; + if (diffsqnorm > 0) + delta = num / (diffsqnorm * max_step_size); + else + delta = 0; + cerr << " D1:" << delta; + //clip delta (enforce margin constraints) + + delta = max(-cur_pair[0]->alpha, min(delta, cur_pair[1]->alpha)); + cerr << " D2:" << delta; + return delta; +} + + +vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo> >* cur_c) +{ + bool DEBUG_SELECT= false; + vector<shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c; + + vector<shared_ptr<HypothesisInfo> > pair; + + if (no_select || optimizer == 2){ //skip heuristic search and return oracle and fear for 1-mira + // if(optimizer == 2) { + pair.push_back(cur_constraint[0]); + pair.push_back(cur_constraint[1]); + return pair; + // } + } + + for(int u=0;u != cur_constraint.size();u++) + { + shared_ptr<HypothesisInfo> max_fear; + + if(DEBUG_SELECT) cerr<< "cur alpha " << u << " " << cur_constraint[u]->alpha; + for(int i=0; i < cur_constraint.size();i++) //select maximal violator + { + if(i != u) + if (!max_fear || cur_constraint[i]->fear > max_fear->fear) + max_fear = cur_constraint[i]; + } + if(!max_fear) return pair; // + + if(DEBUG_SELECT) cerr << " F" << max_fear->fear << endl; + + + if ((cur_constraint[u]->alpha == 0) && (cur_constraint[u]->fear > max_fear->fear + SMO_EPSILON)) + { + for(int i=0; i < cur_constraint.size();i++) //select maximal violator + { + if(i != u) + if (cur_constraint[i]->alpha > 0) + { + pair.push_back(cur_constraint[u]); + pair.push_back(cur_constraint[i]); + cerr << "RETJURN from 1" << endl; + return pair; + } + } + } + if ((cur_constraint[u]->alpha > 0) && (cur_constraint[u]->fear < max_fear->fear - SMO_EPSILON)) + { + for(int i=0; i < cur_constraint.size();i++) //select maximal violator + { + if(i != u) + if (cur_constraint[i]->fear > cur_constraint[u]->fear) + { + pair.push_back(cur_constraint[u]); + pair.push_back(cur_constraint[i]); + return pair; + } + } + } + + } + return pair; //no more constraints to optimize, we're done here + +} + +struct GoodBadOracle { + vector<shared_ptr<HypothesisInfo> > good; + vector<shared_ptr<HypothesisInfo> > bad; +}; + +struct TrainingObserver : public DecoderObserver { + TrainingObserver(const int k, const DocScorer& d, vector<GoodBadOracle>* o, vector<ScoreP>* cbs) : ds(d), oracles(*o), corpus_bleu_sent_stats(*cbs), kbest_size(k) { + // TrainingObserver(const int k, const DocScorer& d, vector<GoodBadOracle>* o) : ds(d), oracles(*o), kbest_size(k) { + + //calculate corpus bleu score from previous iterations 1-best for BLEU gain + if(!pseudo_doc && !sent_approx) + if(cur_pass > 0) + { + ScoreP acc; + for (int ii = 0; ii < corpus_bleu_sent_stats.size(); ii++) { + if (!acc) { acc = corpus_bleu_sent_stats[ii]->GetZero(); } + acc->PlusEquals(*corpus_bleu_sent_stats[ii]); + + } + corpus_bleu_stats = acc; + corpus_bleu_score = acc->ComputeScore(); + } + //corpus_src_length = 0; +} + const DocScorer& ds; + vector<ScoreP>& corpus_bleu_sent_stats; + vector<GoodBadOracle>& oracles; + vector<shared_ptr<HypothesisInfo> > cur_best; + shared_ptr<HypothesisInfo> cur_oracle; + const int kbest_size; + Hypergraph forest; + int cur_sent; + ScoreP corpus_bleu_stats; + float corpus_bleu_score; + + float corpus_src_length; + float curr_src_length; + + const int GetCurrentSent() const { + return cur_sent; + } + + const HypothesisInfo& GetCurrentBestHypothesis() const { + return *cur_best[0]; + } + + const vector<shared_ptr<HypothesisInfo> > GetCurrentBest() const { + return cur_best; + } + + const HypothesisInfo& GetCurrentOracle() const { + return *cur_oracle; + } + + const Hypergraph& GetCurrentForest() const { + return forest; + } + + + virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) { + cur_sent = smeta.GetSentenceID(); + //cerr << "SOURCE " << smeta.GetSourceLength() << endl; + curr_src_length = (float) smeta.GetSourceLength(); + //UpdateOracles(smeta.GetSentenceID(), *hg); + if(unique_kbest) + UpdateOracles<KBest::FilterUnique>(smeta.GetSentenceID(), *hg); + else + UpdateOracles<KBest::NoFilter<std::vector<WordID> > >(smeta.GetSentenceID(), *hg); + forest = *hg; + + } + + shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score, const vector<WordID>& hyp) { + shared_ptr<HypothesisInfo> h(new HypothesisInfo); + h->features = feats; + h->mt_metric = score; + h->hyp = hyp; + return h; + } + + template <class Filter> + void UpdateOracles(int sent_id, const Hypergraph& forest) { + + bool PRINT_LIST= false; + vector<shared_ptr<HypothesisInfo> >& cur_good = oracles[sent_id].good; + vector<shared_ptr<HypothesisInfo> >& cur_bad = oracles[sent_id].bad; + //TODO: look at keeping previous iterations hypothesis lists around + cur_best.clear(); + cur_good.clear(); + cur_bad.clear(); + + vector<shared_ptr<HypothesisInfo> > all_hyp; + + typedef KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,Filter> K; + K kbest(forest,kbest_size); + + //KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size); + for (int i = 0; i < kbest_size; ++i) { + //const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d = + typename K::Derivation *d = + kbest.LazyKthBest(forest.nodes_.size() - 1, i); + if (!d) break; + + float sentscore; + if(cur_pass > 0 && !pseudo_doc && !sent_approx) + { + ScoreP sent_stats = ds[sent_id]->ScoreCandidate(d->yield); + ScoreP corpus_no_best = corpus_bleu_stats->GetZero(); + + corpus_bleu_stats->Subtract(*corpus_bleu_sent_stats[sent_id], &*corpus_no_best); + sent_stats->PlusEquals(*corpus_no_best, 0.5); + + //compute gain from new sentence in 1-best corpus + sentscore = mt_metric_scale * (sent_stats->ComputeScore() - corpus_no_best->ComputeScore());// - corpus_bleu_score); + } + else if(pseudo_doc) //pseudo-corpus smoothing + { + float src_scale = corpus_src_length + curr_src_length; + ScoreP sent_stats = ds[sent_id]->ScoreCandidate(d->yield); + if(!corpus_bleu_stats){ corpus_bleu_stats = sent_stats->GetZero();} + + sent_stats->PlusEquals(*corpus_bleu_stats); + sentscore = mt_metric_scale * src_scale * sent_stats->ComputeScore(); + + } + else //use sentence-level smoothing ( used when cur_pass=0 if not pseudo_doc) + { + + sentscore = mt_metric_scale * (ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore()); + } + + if (invert_score) sentscore *= -1.0; + + if (i < update_list_size){ + if(PRINT_LIST)cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl; + cur_best.push_back( MakeHypothesisInfo(d->feature_values, sentscore, d->yield)); + } + + all_hyp.push_back(MakeHypothesisInfo(d->feature_values, sentscore,d->yield)); //store all hyp to extract hope and fear + } + + if(pseudo_doc){ + //update psuedo-doc stats + string details, details2; + corpus_bleu_stats->ScoreDetails(&details2); + ScoreP sent_stats = ds[sent_id]->ScoreCandidate(cur_best[0]->hyp); + corpus_bleu_stats->PlusEquals(*sent_stats); + + sent_stats->ScoreDetails(&details); + sent_stats = corpus_bleu_stats; + corpus_bleu_stats = sent_stats->GetZero(); + corpus_bleu_stats->PlusEquals(*sent_stats, PSEUDO_SCALE); + + corpus_src_length = PSEUDO_SCALE * (corpus_src_length + curr_src_length); + cerr << "CORP S " << corpus_src_length << " " << curr_src_length << "\n" << details << "\n" << details2 << endl; + } + + + //figure out how many hyps we can keep maximum + int temp_update_size = update_list_size; + if (all_hyp.size() < update_list_size){ temp_update_size = all_hyp.size();} + + //sort all hyps by sentscore (eg. bleu) + sort(all_hyp.begin(),all_hyp.end(),HypothesisCompareB); + + if(PRINT_LIST){ cerr << "Sorting " << endl; for(int u=0;u!=all_hyp.size();u++) cerr << all_hyp[u]->mt_metric << " " << all_hyp[u]->features.dot(dense_weights_g) << endl; } + + if(hope_select == 1) + { + //find hope hypothesis using model + bleu + if (PRINT_LIST) cerr << "HOPE " << endl; + for(int u=0;u!=all_hyp.size();u++) + { + double t_score = all_hyp[u]->features.dot(dense_weights_g); + all_hyp[u]->hope = all_hyp[u]->mt_metric + t_score; + if (PRINT_LIST) cerr << all_hyp[u]->mt_metric << " H:" << all_hyp[u]->hope << " S:" << t_score << endl; + + } + + //sort hyps by hope score + sort(all_hyp.begin(),all_hyp.end(),HopeCompareB); + } + + //assign cur_good the sorted list + cur_good.insert(cur_good.begin(), all_hyp.begin(), all_hyp.begin()+temp_update_size); + if(PRINT_LIST) { cerr << "GOOD" << endl; for(int u=0;u!=cur_good.size();u++) cerr << cur_good[u]->mt_metric << " " << cur_good[u]->hope << endl;} + + shared_ptr<HypothesisInfo>& oracleN = cur_good[0]; + + + if(fear_select == 1){ //compute fear hyps with model - bleu + if (PRINT_LIST) cerr << "FEAR " << endl; + double hope_score = oracleN->features.dot(dense_weights_g); + + if (PRINT_LIST) cerr << "hope score " << hope_score << endl; + for(int u=0;u!=all_hyp.size();u++) + { + double t_score = all_hyp[u]->features.dot(dense_weights_g); + //all_hyp[u]->fear = -1*all_hyp[u]->mt_metric - hope_score + t_score; + + /* all_hyp[u]->fear = -1*all_hyp[u]->mt_metric - -1*cur_oracle->mt_metric - hope_score + t_score; //relative loss + all_hyp[u]->oracle_loss = -1*all_hyp[u]->mt_metric - -1*cur_oracle->mt_metric; + all_hyp[u]->oracle_feat_diff = cur_oracle->features - all_hyp[u]->features;*/ + + all_hyp[u]->fear = -1*all_hyp[u]->mt_metric + 1*oracleN->mt_metric - hope_score + t_score; //relative loss + all_hyp[u]->oracle_loss = -1*all_hyp[u]->mt_metric + 1*oracleN->mt_metric; + all_hyp[u]->oracle_feat_diff = oracleN->features - all_hyp[u]->features; + all_hyp[u]->oracleN=oracleN; + // all_hyp[u]->fear = -1 * all_hyp[u]->mt_metric + t_score; + if (PRINT_LIST) cerr << all_hyp[u]->mt_metric << " H:" << all_hyp[u]->hope << " F:" << all_hyp[u]->fear << endl; + + } + + sort(all_hyp.begin(),all_hyp.end(),FearCompareB); + + cur_bad.insert(cur_bad.begin(), all_hyp.begin(), all_hyp.begin()+temp_update_size); + } + else if(fear_select == 2) //select fear based on cost + { + cur_bad.insert(cur_bad.begin(), all_hyp.end()-temp_update_size, all_hyp.end()); + reverse(cur_bad.begin(),cur_bad.end()); + } + else //pred-based, fear_select = 3 + { + sort(all_hyp.begin(),all_hyp.end(),FearComparePred); + cur_bad.insert(cur_bad.begin(), all_hyp.begin(), all_hyp.begin()+temp_update_size); + } + + + if(PRINT_LIST){ cerr<< "BAD"<<endl; for(int u=0;u!=cur_bad.size();u++) cerr << cur_bad[u]->mt_metric << " H:" << cur_bad[u]->hope << " F:" << cur_bad[u]->fear << endl;} + + cerr << "GOOD (BEST): " << cur_good[0]->mt_metric << endl; + cerr << " CUR: " << cur_best[0]->mt_metric << endl; + cerr << " BAD (WORST): " << cur_bad[0]->mt_metric << endl; + } +}; + +void ReadTrainingCorpus(const string& fname, vector<string>* c) { + + + ReadFile rf(fname); + istream& in = *rf.stream(); + string line; + while(in) { + getline(in, line); + if (!in) break; + c->push_back(line); + } +} + +void ReadPastTranslationForScore(const int cur_pass, vector<ScoreP>* c, DocScorer& ds, const string& od) +{ + cerr << "Reading BLEU gain file "; + string fname; + if(cur_pass == 0) + { + fname = od + "/run.raw.init"; + } + else + { + int last_pass = cur_pass - 1; + fname = od + "/run.raw." + boost::lexical_cast<std::string>(last_pass) + ".B"; + } + cerr << fname << "\n"; + ReadFile rf(fname); + istream& in = *rf.stream(); + ScoreP acc; + string line; + int lc = 0; + while(in) { + getline(in, line); + if (line.empty() && !in) break; + vector<WordID> sent; + TD::ConvertSentence(line, &sent); + ScoreP sentscore = ds[lc]->ScoreCandidate(sent); + c->push_back(sentscore); + if (!acc) { acc = sentscore->GetZero(); } + acc->PlusEquals(*sentscore); + ++lc; + + } + + + assert(lc > 0); + float score = acc->ComputeScore(); + string details; + acc->ScoreDetails(&details); + cerr << "INIT RUN " << details << score << endl; + +} + + +int main(int argc, char** argv) { + register_feature_functions(); + SetSilent(true); // turn off verbose decoder output + + po::variables_map conf; + if (!InitCommandLine(argc, argv, &conf)) return 1; + + if (conf.count("random_seed")) + rng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); + else + rng.reset(new MT19937); + + vector<string> corpus; + //ReadTrainingCorpus(conf["source"].as<string>(), &corpus); + + const string metric_name = conf["mt_metric"].as<string>(); + optimizer = conf["optimizer"].as<int>(); + fear_select = conf["fear"].as<int>(); + hope_select = conf["hope"].as<int>(); + mt_metric_scale = conf["mt_metric_scale"].as<double>(); + approx_score = conf.count("approx_score"); + no_reweight = conf.count("no_reweight"); + no_select = conf.count("no_select"); + update_list_size = conf["update_k_best"].as<int>(); + unique_kbest = conf.count("unique_k_best"); + pseudo_doc = conf.count("pseudo_doc"); + sent_approx = conf.count("sent_approx"); + cerr << "PSEUDO " << pseudo_doc << " SENT " << sent_approx << endl; + if(pseudo_doc) + mt_metric_scale=1; + + const string weights_dir = conf["weights_output"].as<string>(); + const string output_dir = conf["output_dir"].as<string>(); + ScoreType type = ScoreTypeFromString(metric_name); + + //establish metric used for tuning + if (type == TER) { + invert_score = true; + // approx_score = false; + } else { + invert_score = false; + } + + //load references + DocScorer ds(type, conf["reference"].as<vector<string> >(), ""); + cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl; + vector<ScoreP> corpus_bleu_sent_stats; + + //check training pass,if >0, then use previous iterations corpus bleu stats + cur_pass = conf["pass"].as<int>(); + if(cur_pass > 0) + { + ReadPastTranslationForScore(cur_pass, &corpus_bleu_sent_stats, ds, output_dir); + } + /* if (ds.size() != corpus.size()) { + cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n"; + return 1; + }*/ + cerr << "Optimizing with " << optimizer << endl; + // load initial weights + /*Weights weights; + weights.InitFromFile(conf["input_weights"].as<string>()); + SparseVector<double> lambdas; + weights.InitSparseVector(&lambdas); + */ + + + + ReadFile ini_rf(conf["decoder_config"].as<string>()); + Decoder decoder(ini_rf.stream()); + + vector<weight_t>& dense_weights = decoder.CurrentWeightVector(); + + SparseVector<weight_t> lambdas; + Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights); + Weights::InitSparseVector(dense_weights, &lambdas); + + const string input = decoder.GetConf()["input"].as<string>(); + //const bool show_feature_dictionary = decoder.GetConf().count("show_feature_dictionary"); + if (!SILENT) cerr << "Reading input from " << ((input == "-") ? "STDIN" : input.c_str()) << endl; + ReadFile in_read(input); + istream *in = in_read.stream(); + assert(*in); + string buf; + + const double max_step_size = conf["max_step_size"].as<double>(); + + + // assert(corpus.size() > 0); + vector<GoodBadOracle> oracles(ds.size()); + + TrainingObserver observer(conf["k_best_size"].as<int>(), ds, &oracles, &corpus_bleu_sent_stats); + + int cur_sent = 0; + int lcount = 0; + double objective=0; + double tot_loss = 0; + int dots = 0; + // int cur_pass = 1; + // vector<double> dense_weights; + SparseVector<double> tot; + SparseVector<double> final_tot; + // tot += lambdas; // initial weights + // lcount++; // count for initial weights + + //string msg = "# MIRA tuned weights"; + // while (cur_pass <= max_iteration) { + SparseVector<double> old_lambdas = lambdas; + tot.clear(); + tot += lambdas; + cerr << "PASS " << cur_pass << " " << endl << lambdas << endl; + ScoreP acc, acc_h, acc_f; + + while(*in) { + getline(*in, buf); + if (buf.empty()) continue; + //TODO: allow batch updating + lambdas.init_vector(&dense_weights); + dense_weights_g = dense_weights; + decoder.SetId(cur_sent); + decoder.Decode(buf, &observer); // decode the sentence, calling Notify to get the hope,fear, and model best hyps. + + cur_sent = observer.GetCurrentSent(); + cerr << "SENT: " << cur_sent << endl; + const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis(); + const HypothesisInfo& cur_good = *oracles[cur_sent].good[0]; + const HypothesisInfo& cur_bad = *oracles[cur_sent].bad[0]; + + vector<shared_ptr<HypothesisInfo> >& cur_good_v = oracles[cur_sent].good; + vector<shared_ptr<HypothesisInfo> >& cur_bad_v = oracles[cur_sent].bad; + vector<shared_ptr<HypothesisInfo> > cur_best_v = observer.GetCurrentBest(); + + tot_loss += cur_hyp.mt_metric; + + //score hyps to be able to compute corpus level bleu after we finish this iteration through the corpus + ScoreP sentscore = ds[cur_sent]->ScoreCandidate(cur_hyp.hyp); + if (!acc) { acc = sentscore->GetZero(); } + acc->PlusEquals(*sentscore); + + ScoreP hope_sentscore = ds[cur_sent]->ScoreCandidate(cur_good.hyp); + if (!acc_h) { acc_h = hope_sentscore->GetZero(); } + acc_h->PlusEquals(*hope_sentscore); + + ScoreP fear_sentscore = ds[cur_sent]->ScoreCandidate(cur_bad.hyp); + if (!acc_f) { acc_f = fear_sentscore->GetZero(); } + acc_f->PlusEquals(*fear_sentscore); + + if(optimizer == 4) { //passive-aggresive update (single dual coordinate step) + + double margin = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights); + double mt_loss = (cur_good.mt_metric - cur_bad.mt_metric); + const double loss = margin + mt_loss; + cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << " " << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) <<endl; + if (loss > 0.0 || !checkloss) { + SparseVector<double> diff = cur_good.features; + diff -= cur_bad.features; + + double diffsqnorm = diff.l2norm_sq(); + double delta; + if (diffsqnorm > 0) + delta = loss / (diffsqnorm); + else + delta = 0; + + if (delta > max_step_size) delta = max_step_size; + lambdas += (cur_good.features * delta); + lambdas -= (cur_bad.features * delta); + + } + } + else if(optimizer == 1) //sgd - nonadapted step size + { + + lambdas += (cur_good.features) * max_step_size; + lambdas -= (cur_bad.features) * max_step_size; + } + else if(optimizer == 5) //full mira with n-best list of constraints from hope, fear, model best + { + vector<shared_ptr<HypothesisInfo> > cur_constraint; + cur_constraint.insert(cur_constraint.begin(), cur_bad_v.begin(), cur_bad_v.end()); + cur_constraint.insert(cur_constraint.begin(), cur_best_v.begin(), cur_best_v.end()); + cur_constraint.insert(cur_constraint.begin(), cur_good_v.begin(), cur_good_v.end()); + + bool optimize_again; + vector<shared_ptr<HypothesisInfo> > cur_pair; + //SMO + for(int u=0;u!=cur_constraint.size();u++) + cur_constraint[u]->alpha =0; + + cur_constraint[0]->alpha =1; //set oracle to alpha=1 + + cerr <<"Optimizing with " << cur_constraint.size() << " constraints" << endl; + int smo_iter = MAX_SMO, smo_iter2 = MAX_SMO; + int iter, iter2 =0; + bool DEBUG_SMO = false; + while (iter2 < smo_iter2) + { + iter =0; + while (iter < smo_iter) + { + optimize_again = true; + for (int i = 0; i< cur_constraint.size(); i++) + for (int j = i+1; j< cur_constraint.size(); j++) + { + if(DEBUG_SMO) cerr << "start " << i << " " << j << endl; + cur_pair.clear(); + cur_pair.push_back(cur_constraint[j]); + cur_pair.push_back(cur_constraint[i]); + double delta = ComputeDelta(&cur_pair,max_step_size, dense_weights); + + if (delta == 0) optimize_again = false; + cur_constraint[j]->alpha += delta; + cur_constraint[i]->alpha -= delta; + double step_size = delta * max_step_size; + + lambdas += (cur_constraint[i]->features) * step_size; + lambdas -= (cur_constraint[j]->features) * step_size; + if(DEBUG_SMO) cerr << "SMO opt " << iter << " " << i << " " << j << " " << delta << " " << cur_pair[0]->alpha << " " << cur_pair[1]->alpha << endl; + } + iter++; + + if(!optimize_again) + { + iter = MAX_SMO; + cerr << "Optimization stopped, delta =0" << endl; + } + } + iter2++; + } + } + else if(optimizer == 2 || optimizer == 3) //PA and Cutting Plane MIRA update + { + bool DEBUG_SMO= true; + vector<shared_ptr<HypothesisInfo> > cur_constraint; + cur_constraint.push_back(cur_good_v[0]); //add oracle to constraint set + bool optimize_again = true; + int cut_plane_calls = 0; + while (optimize_again) + { + if(DEBUG_SMO) cerr<< "optimize again: " << optimize_again << endl; + if(optimizer == 2){ //PA + cur_constraint.push_back(cur_bad_v[0]); + + //check if we have a violation + if(!(cur_constraint[1]->fear > cur_constraint[0]->fear + SMO_EPSILON)) + { + optimize_again = false; + cerr << "Constraint not violated" << endl; + } + } + else + { //cutting plane to add constraints + if(DEBUG_SMO) cerr<< "Cutting Plane " << cut_plane_calls << " with " << lambdas << endl; + optimize_again = false; + cut_plane_calls++; + CuttingPlane(&cur_constraint, &optimize_again, oracles[cur_sent].bad, dense_weights); + if (cut_plane_calls >= MAX_SMO) optimize_again = false; + } + + if(optimize_again) + { + //SMO + for(int u=0;u!=cur_constraint.size();u++) + { + cur_constraint[u]->alpha =0; + } + cur_constraint[0]->alpha = 1; + cerr <<" Optimizing with " << cur_constraint.size() << " constraints" << endl; + int smo_iter = MAX_SMO; + int iter =0; + while (iter < smo_iter) + { + //select pair to optimize from constraint set + vector<shared_ptr<HypothesisInfo> > cur_pair = SelectPair(&cur_constraint); + + if(cur_pair.empty()){ + iter=MAX_SMO; + cerr << "Undefined pair " << endl; + continue; + } //pair is undefined so we are done with this smo + + double delta = ComputeDelta(&cur_pair,max_step_size, dense_weights); + + cur_pair[0]->alpha += delta; + cur_pair[1]->alpha -= delta; + double step_size = delta * max_step_size; + cerr << "step " << step_size << endl; + + lambdas += (cur_pair[1]->features) * step_size; + lambdas -= (cur_pair[0]->features) * step_size; + cerr << " Lambdas " << lambdas << endl; + //reload weights based on update + + dense_weights.clear(); + lambdas.init_vector(&dense_weights); + dense_weights_g = dense_weights; + iter++; + + if(DEBUG_SMO) cerr << "SMO opt " << iter << " " << delta << " " << cur_pair[0]->alpha << " " << cur_pair[1]->alpha << endl; + if(no_select) //don't use selection heuristic to determine when to stop SMO, rather just when delta =0 + if (delta == 0) iter = MAX_SMO; + + //only perform one dual coordinate ascent step + if(optimizer == 2) + { + optimize_again = false; + iter = MAX_SMO; + } + } + if(optimizer == 3) + { + if(!no_reweight) //reweight the forest and select a new k-best + { + if(DEBUG_SMO) cerr<< "Decoding with new weights -- now orac are " << oracles[cur_sent].good.size() << endl; + Hypergraph hg = observer.GetCurrentForest(); + hg.Reweight(dense_weights); + if(unique_kbest) + observer.UpdateOracles<KBest::FilterUnique>(cur_sent, hg); + else + observer.UpdateOracles<KBest::NoFilter<std::vector<WordID> > >(cur_sent, hg); + } + } + } + + } + + //print objective after this sentence + double lambda_change = (lambdas - old_lambdas).l2norm_sq(); + double max_fear = cur_constraint[cur_constraint.size()-1]->fear; + double temp_objective = 0.5 * lambda_change;// + max_step_size * max_fear; + + for(int u=0;u!=cur_constraint.size();u++) + { + cerr << cur_constraint[u]->alpha << " " << cur_constraint[u]->hope << " " << cur_constraint[u]->fear << endl; + temp_objective += cur_constraint[u]->alpha * cur_constraint[u]->fear; + } + objective += temp_objective; + + cerr << "SENT OBJ: " << temp_objective << " NEW OBJ: " << objective << endl; + } + + + if ((cur_sent * 40 / ds.size()) > dots) { ++dots; cerr << '.'; } + tot += lambdas; + ++lcount; + cur_sent++; + + cout << TD::GetString(cur_good_v[0]->hyp) << " ||| " << TD::GetString(cur_best_v[0]->hyp) << " ||| " << TD::GetString(cur_bad_v[0]->hyp) << endl; + + } + + cerr << "FINAL OBJECTIVE: "<< objective << endl; + final_tot += tot; + cerr << "Translated " << lcount << " sentences " << endl; + cerr << " [AVG METRIC LAST PASS=" << (tot_loss / lcount) << "]\n"; + tot_loss = 0; + + int node_id = rng->next() * 100000; + cerr << " Writing weights to " << node_id << endl; + Weights::ShowLargestFeatures(dense_weights); + dots = 0; + ostringstream os; + os << weights_dir << "/weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "." << node_id << ".gz"; + string msg = "# MIRA tuned weights ||| " + boost::lexical_cast<std::string>(node_id) + " ||| " + boost::lexical_cast<std::string>(lcount); + //Weights.InitFromVector(lambdas); + lambdas.init_vector(&dense_weights); + Weights::WriteToFile(os.str(), dense_weights, true, &msg); + + SparseVector<double> x = tot; + x /= lcount; + ostringstream sa; + string msga = "# MIRA tuned weights AVERAGED ||| " + boost::lexical_cast<std::string>(node_id) + " ||| " + boost::lexical_cast<std::string>(lcount); + sa << weights_dir << "/weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "." << node_id << "-avg.gz"; + x.init_vector(&dense_weights); + Weights::WriteToFile(sa.str(), dense_weights, true, &msga); + + + cerr << "Optimization complete.\n"; + return 0; +} + |