From 045bad698d84b1a0a54bafa9152dc4436f1cbb95 Mon Sep 17 00:00:00 2001
From: vlade <vlad@nod.umiacs.umd.edu>
Date: Sat, 13 Apr 2013 00:48:10 -0400
Subject: inital commit of mira code
---
training/mira/kbest_mirav5.cc | 1148 +++++++++++++++++++++++++++++++++++++++++
training/mira/run_mira.pl | 548 ++++++++++++++++++++
2 files changed, 1696 insertions(+)
create mode 100644 training/mira/kbest_mirav5.cc
create mode 100755 training/mira/run_mira.pl
(limited to 'training')
diff --git a/training/mira/kbest_mirav5.cc b/training/mira/kbest_mirav5.cc
new file mode 100644
index 00000000..cea5cf67
--- /dev/null
+++ b/training/mira/kbest_mirav5.cc
@@ -0,0 +1,1148 @@
+#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;
+
+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")
+ ("passes,p", po::value<int>()->default_value(15), "Number of passes 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, mira 1-fear=2, full mira w/ cutting plane=3, full mira w/ nbest list=5, local update=4)")
+ ("fear,f",po::value<int>()->default_value(1), "Fear selection (model-cost=1, max-cost=2, pred-base=3)")
+ ("hope,h",po::value<int>()->default_value(1), "Hope selection (model+cost=1, max-cost=2, local-cost=3)")
+ ("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")
+ ("approx_score,a", "Use smoothed sentence-level 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;
+ //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);
+
+ 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;
+ //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);
+
+ }
+ //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 margin = cur_pair[1]->features.dot(dense_weights) - cur_pair[0]->features.dot(dense_weights);
+ // double loss = cur_pair[1]->oracle_loss; //good.mt_metric - cur_bad.mt_metric);
+ //const double num = margin + loss;
+
+ //cerr << "Compute Delta " << loss << " " << margin << " ";
+
+ // double margin = cur_pair[0]->features.dot(dense_weights) - cur_pair[1]->features.dot(dense_weights); //TODO: is it a problem that new oracle is used in diff?
+/* 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)
+ 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(approx_score)
+ {
+
+ if(cur_pass > 0 && !pseudo_doc)
+ {
+ 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)
+ {
+ //cerr << "CORP:" << corpus_bleu_score << " NEW:" << sent_stats->ComputeScore() << " sentscore:" << sentscore << endl;
+
+ //-----pseudo-corpus approach
+ 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
+ {
+ //cerr << "Using sentence-level approximation - PASS - " << boost::lexical_cast<std::string>(cur_pass) << endl;
+ //approx style of computation, used for 0th iteration
+ sentscore = mt_metric_scale * (ds[sent_id]->ScoreCandidate(d->yield)->ComputeSentScore());
+
+ //use pseudo-doc
+ }
+
+
+ }
+ else
+ {
+ sentscore = mt_metric_scale * (ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore());
+ }
+
+ if (invert_score) sentscore *= -1.0;
+ //cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << " " << approx_sentscore << endl;
+
+ if (i < update_list_size){
+ if (i == 0) //take cur best and add its bleu statistics counts to the pseudo-doc
+ { }
+ 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 oracle best and worst
+
+ }
+
+ 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 (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(optimizer != 4 )
+ 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;}
+ /* if (!cur_oracle) { cur_oracle = cur_good[0];
+ cerr << "Set oracle " << cur_oracle->hope << " " << cur_oracle->fear << " " << cur_oracle->mt_metric << endl; }
+ else {
+ cerr << "Stay oracle " << cur_oracle->hope << " " << cur_oracle->fear << " " << cur_oracle->mt_metric << endl; } */
+
+ shared_ptr<HypothesisInfo>& oracleN = cur_good[0];
+ //if(optimizer != 4){
+ if(fear_select == 1){
+ //compute fear hyps
+ if (PRINT_LIST) cerr << "FEAR " << endl;
+ double hope_score = oracleN->features.dot(dense_weights_g);
+ //double hope_score = cur_oracle->features.dot(dense_weights);
+ 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 = true;
+
+ 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["passes"].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;
+ //for (cur_sent = 0; cur_sent < corpus.size(); cur_sent++) {
+
+ cerr << "SENT: " << cur_sent << endl;
+ //TODO: allow batch updating
+ //dense_weights.clear();
+ //weights.InitFromVector(lambdas);
+ //weights.InitVector(&dense_weights);
+ //decoder.SetWeights(dense_weights);
+ 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();
+ 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) { //single dual coordinate update, cur_good selected on BLEU score only (not model+BLEU)
+ // if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) {
+
+ 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) {
+ 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;
+
+ //double step_size = loss / diff.l2norm_sq();
+ cerr << loss << " " << delta << " " << diff << endl;
+ if (delta > max_step_size) delta = max_step_size;
+ lambdas += (cur_good.features * delta);
+ lambdas -= (cur_bad.features * delta);
+ //cerr << "L: " << lambdas << endl;
+ // }
+ // }
+ }
+ else if(optimizer == 1) //sgd - nonadapted step size
+ {
+
+ lambdas += (cur_good.features) * max_step_size;
+ lambdas -= (cur_bad.features) * max_step_size;
+ }
+ //cerr << "L: " << lambdas << endl;
+ else if(optimizer == 5) //full mira with n-best list of constraints from oracle, fear, 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 = 10, smo_iter2 = 10;
+ 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_pair[0]->alpha += delta;
+ // cur_pair[1]->alpha -= delta;
+ cur_constraint[j]->alpha += delta;
+ cur_constraint[i]->alpha -= delta;
+ double step_size = delta * max_step_size;
+ /*lambdas += (cur_pair[1]->features) * step_size;
+ lambdas -= (cur_pair[0]->features) * 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;
+
+ //reload weights based on update
+ /*dense_weights.clear();
+ weights.InitFromVector(lambdas);
+ weights.InitVector(&dense_weights);*/
+ }
+ iter++;
+
+ if(!optimize_again)
+ {
+ iter = 100;
+ cerr << "Optimization stopped, delta =0" << endl;
+ }
+
+
+ }
+ iter2++;
+ }
+
+
+ }
+ else if(optimizer == 2 || optimizer == 3) //1-fear and cutting plane mira
+ {
+ 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){ //1-fear
+ 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_good_v[0]->alpha = 1; cur_bad_v[0]->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 num = cur_good_v[0]->fear - cur_bad_v[0]->fear;
+ /*double loss = cur_good_v[0]->oracle_loss - cur_bad_v[0]->oracle_loss;
+ double margin = cur_good_v[0]->oracle_feat_diff.dot(dense_weights) - cur_bad_v[0]->oracle_feat_diff.dot(dense_weights);
+ double num = loss - margin;
+ SparseVector<double> diff = cur_good_v[0]->features;
+ diff -= cur_bad_v[0]->features;
+ double delta = num / (diff.l2norm_sq() * max_step_size);
+ delta = max(-cur_good_v[0]->alpha, min(delta, cur_bad_v[0]->alpha));
+ cur_good_v[0]->alpha += delta;
+ cur_bad_v[0]->alpha -= delta;
+ double step_size = delta * max_step_size;
+ lambdas += (cur_bad_v[0]->features) * step_size;
+ lambdas -= (cur_good_v[0]->features) * step_size;
+ */
+
+ 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;
+ /* lambdas += (cur_pair[1]->oracle_feat_diff) * step_size;
+ lambdas -= (cur_pair[0]->oracle_feat_diff) * step_size;*/
+
+ cerr << "step " << step_size << endl;
+ double alpha_sum=0;
+ SparseVector<double> temp_lambdas = lambdas;
+
+ for(int u=0;u!=cur_constraint.size();u++)
+ {
+ cerr << cur_constraint[u]->alpha << " " << cur_constraint[u]->hope << endl;
+ temp_lambdas += (cur_constraint[u]->oracleN->features-cur_constraint[u]->features) * cur_constraint[u]->alpha * step_size;
+ alpha_sum += cur_constraint[u]->alpha;
+ }
+ cerr << "Alpha sum " << alpha_sum << " " << temp_lambdas << 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();
+ //weights.InitFromVector(lambdas);
+ //weights.InitVector(&dense_weights);
+ 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;
+ // cerr << "SMO opt " << iter << " " << delta << " " << cur_good_v[0]->alpha << " " << cur_bad_v[0]->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)
+ {
+ 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);
+ //observer.UpdateOracles(cur_sent, hg);
+ 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;
+
+ //clear good/bad lists from oracles for this sentences - you want to keep them around for things
+
+ // oracles[cur_sent].good.clear();
+ //oracles[cur_sent].bad.clear();
+ }
+
+ 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;
+ /*
+ float corpus_score = acc->ComputeScore();
+ string corpus_details;
+ acc->ScoreDetails(&corpus_details);
+ cerr << "MODEL " << corpus_details << endl;
+ cout << corpus_score << endl;
+
+ corpus_score = acc_h->ComputeScore();
+ acc_h->ScoreDetails(&corpus_details);
+ cerr << "HOPE " << corpus_details << endl;
+ cout << corpus_score << endl;
+
+ corpus_score = acc_f->ComputeScore();
+ acc_f->ScoreDetails(&corpus_details);
+ cerr << "FEAR " << corpus_details << endl;
+ cout << corpus_score << endl;
+ */
+ 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";
+ //Weights ww;
+ //ww.InitFromVector(x);
+ x.init_vector(&dense_weights);
+ Weights::WriteToFile(sa.str(), dense_weights, true, &msga);
+
+ //assign averaged lambdas to initialize next iteration
+ //lambdas = x;
+
+ /* double lambda_change = (old_lambdas - lambdas).l2norm_sq();
+ cerr << "Change in lambda " << lambda_change << endl;
+
+ if ( lambda_change < EPSILON)
+ {
+ cur_pass = max_iteration;
+ cerr << "Weights converged - breaking" << endl;
+ }
+
+ ++cur_pass;
+ */
+
+ //} iteration while loop
+
+ /* cerr << endl;
+ weights.WriteToFile("weights.mira-final.gz", true, &msg);
+ final_tot /= (lcount + 1);//max_iteration);
+ tot /= (corpus.size() + 1);
+ weights.InitFromVector(final_tot);
+ cerr << tot << "||||" << final_tot << endl;
+ msg = "# MIRA tuned weights (averaged vector)";
+ weights.WriteToFile("weights.mira-final-avg.gz", true, &msg);
+ */
+ cerr << "Optimization complete.\\AVERAGED WEIGHTS: weights.mira-final-avg.gz\n";
+ return 0;
+}
+
diff --git a/training/mira/run_mira.pl b/training/mira/run_mira.pl
new file mode 100755
index 00000000..f4d61407
--- /dev/null
+++ b/training/mira/run_mira.pl
@@ -0,0 +1,548 @@
+#!/usr/bin/env perl
+use strict;
+my @ORIG_ARGV=@ARGV;
+use Cwd qw(getcwd);
+my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0));
+push @INC, $SCRIPT_DIR, "$SCRIPT_DIR/../environment"; }
+
+# Skip local config (used for distributing jobs) if we're running in local-only mode
+use LocalConfig;
+use Getopt::Long;
+use IPC::Open2;
+use POSIX ":sys_wait_h";
+my $QSUB_CMD = qsub_args(mert_memory());
+
+require "libcall.pl";
+
+
+my $srcFile;
+my $refFiles;
+my $bin_dir = $SCRIPT_DIR;
+die "Bin directory $bin_dir missing/inaccessible" unless -d $bin_dir;
+my $FAST_SCORE="$bin_dir/../mteval/fast_score";
+die "Can't execute $FAST_SCORE" unless -x $FAST_SCORE;
+
+my $iteration = 0.0;
+my $max_iterations = 6;
+my $metric = "ibm_bleu";
+my $iniFile;
+my $weights;
+my $initialWeights;
+my $decode_nodes = 1; # number of decode nodes
+my $pmem = "1g";
+my $dir;
+
+my $SCORER = $FAST_SCORE;
+my $local_server = "$bin_dir/local_parallelize.pl";
+my $parallelize = "$bin_dir/../dpmert/parallelize.pl";
+my $libcall = "$bin_dir/../dpmert/libcall.pl";
+my $sentserver = "$bin_dir/../dpmert/sentserver";
+my $sentclient = "$bin_dir/../dpmert/sentclient";
+my $run_local_server = 0;
+my $run_local = 0;
+my $usefork;
+my $pass_suffix = '';
+
+my $cdec ="$bin_dir/kbest_mirav5"; #"$bin_dir/kbest_mira_rmmv2"; #"$bin_dir/kbest_mira_lv";
+
+#my $cdec ="$bin_dir/kbest_mira_rmmv2"; #"$bin_dir/kbest_mirav5"; #"$bin_dir/kbest_mira_rmmv2"; #"$bin_dir/kbest_mira_lv";
+die "Can't find decoder in $cdec" unless -x $cdec;
+my $decoder = $cdec;
+my $decoderOpt;
+my $update_size=250;
+my $approx_score;
+my $kbest_size=250;
+my $metric_scale=1;
+my $optimizer=2;
+my $disable_clean = 0;
+my $use_make; # use make to parallelize line search
+my $density_prune;
+my $cpbin=1;
+my $help = 0;
+my $epsilon = 0.0001;
+my $step_size = 0.01;
+my $gpref;
+my $unique_kbest;
+my $freeze;
+my $latent;
+my $sample_max;
+my $hopes=1;
+my $fears=1;
+
+my $range = 35000;
+my $minimum = 15000;
+my $portn = int(rand($range)) + $minimum;
+
+
+# Process command-line options
+Getopt::Long::Configure("no_auto_abbrev");
+if (GetOptions(
+ "decoder=s" => \$decoderOpt,
+ "decode-nodes=i" => \$decode_nodes,
+ "density-prune=f" => \$density_prune,
+ "dont-clean" => \$disable_clean,
+ "pass-suffix=s" => \$pass_suffix,
+ "use-fork" => \$usefork,
+ "epsilon=s" => \$epsilon,
+ "help" => \$help,
+ "local" => \$run_local,
+ "local_server" => \$run_local_server,
+ "use-make=i" => \$use_make,
+ "max-iterations=i" => \$max_iterations,
+ "pmem=s" => \$pmem,
+ "cpbin!" => \$cpbin,
+ "ref-files=s" => \$refFiles,
+ "metric=s" => \$metric,
+ "source-file=s" => \$srcFile,
+ "weights=s" => \$initialWeights,
+ "optimizer=i" => \$optimizer,
+ "metric-scale=i" => \$metric_scale,
+ "kbest-size=i" => \$kbest_size,
+ "update-size=i" => \$update_size,
+ "step-size=f" => \$step_size,
+ "hope-select=i" => \$hopes,
+ "fear-select=i" => \$fears,
+ "approx-score" => \$approx_score,
+ "unique-kbest" => \$unique_kbest,
+ "latent" => \$latent,
+ "sample-max=i" => \$sample_max,
+ "grammar-prefix=s" => \$gpref,
+ "freeze" => \$freeze,
+ "workdir=s" => \$dir,
+ ) == 0 || @ARGV!=1 || $help) {
+ print_help();
+ exit;
+}
+
+($iniFile) = @ARGV;
+
+
+sub write_config;
+sub enseg;
+sub print_help;
+
+my $nodelist;
+my $host =check_output("hostname"); chomp $host;
+my $bleu;
+my $interval_count = 0;
+my $logfile;
+my $projected_score;
+
+
+#my $refs_comma_sep = get_comma_sep_refs($refFiles);
+my $refs_comma_sep = get_comma_sep_refs('r',$refFiles);
+
+#my $refs_comma_sep_4cdec = get_comma_sep_refs_4cdec($refFiles);
+
+unless ($dir){
+ $dir = "mira";
+}
+unless ($dir =~ /^\//){ # convert relative path to absolute path
+ my $basedir = check_output("pwd");
+ chomp $basedir;
+ $dir = "$basedir/$dir";
+}
+
+if ($decoderOpt){ $decoder = $decoderOpt; }
+
+# Initializations and helper functions
+srand;
+
+my @childpids = ();
+my @cleanupcmds = ();
+
+sub cleanup {
+ print STDERR "Cleanup...\n";
+ for my $pid (@childpids){ unchecked_call("kill $pid"); }
+ for my $cmd (@cleanupcmds){ unchecked_call("$cmd"); }
+ exit 1;
+};
+
+# Always call cleanup, no matter how we exit
+*CORE::GLOBAL::exit =
+ sub{ cleanup(); };
+$SIG{INT} = "cleanup";
+$SIG{TERM} = "cleanup";
+$SIG{HUP} = "cleanup";
+
+
+my $decoderBase = check_output("basename $decoder"); chomp $decoderBase;
+my $newIniFile = "$dir/$decoderBase.ini";
+my $inputFileName = "$dir/input";
+my $user = $ENV{"USER"};
+
+
+# process ini file
+-e $iniFile || die "Error: could not open $iniFile for reading\n";
+open(INI, $iniFile);
+
+use File::Basename qw(basename);
+#pass bindir, refs to vars holding bin
+sub modbin {
+ local $_;
+ my $bindir=shift;
+ check_call("mkdir -p $bindir");
+ -d $bindir || die "couldn't make bindir $bindir";
+ for (@_) {
+ my $src=$$_;
+ $$_="$bindir/".basename($src);
+ check_call("cp -p $src $$_");
+ }
+}
+sub dirsize {
+ opendir ISEMPTY,$_[0];
+ return scalar(readdir(ISEMPTY))-1;
+}
+
+
+
+
+if (-e $dir && dirsize($dir)>1 && -e "$dir/weights" ){ # allow preexisting logfile, binaries, but not dist-vest.pl outputs
+ die "ERROR: working dir $dir already exists\n\n";
+} else {
+ -e $dir || mkdir $dir;
+ mkdir "$dir/scripts";
+ my $cmdfile="$dir/rerun-mira.sh";
+ open CMD,'>',$cmdfile;
+ print CMD "cd ",&getcwd,"\n";
+ my $cline=&cmdline."\n";
+ print CMD $cline;
+ close CMD;
+ print STDERR $cline;
+ chmod(0755,$cmdfile);
+ unless (-e $initialWeights) {
+ print STDERR "Please specify an initial weights file with --initial-weights\n";
+ print_help();
+ exit;
+ }
+ check_call("cp $initialWeights $dir/weights.0");
+ die "Can't find weights.0" unless (-e "$dir/weights.0");
+}
+write_config(*STDERR);
+
+# Generate initial files and values
+check_call("cp $iniFile $newIniFile");
+$iniFile = $newIniFile;
+
+my $newsrc = "$dir/dev.input";
+enseg($srcFile, $newsrc, $gpref);
+
+$srcFile = $newsrc;
+my $devSize = 0;
+open F, "<$srcFile" or die "Can't read $srcFile: $!";
+while(<F>) { $devSize++; }
+close F;
+
+my $lastPScore = 0;
+my $lastWeightsFile;
+
+# main optimization loop
+#while (1){
+for (my $opt_iter=0; $opt_iter<$max_iterations; $opt_iter++) {
+
+ print STDERR "\n\nITERATION $opt_iter\n==========\n";
+ print STDERR "Using port $portn\n";
+
+ # iteration-specific files
+ my $runFile="$dir/run.raw.$opt_iter";
+ my $onebestFile="$dir/1best.$opt_iter";
+ my $logdir="$dir/logs.$opt_iter";
+ my $decoderLog="$logdir/decoder.sentserver.log.$opt_iter";
+ my $scorerLog="$logdir/scorer.log.$opt_iter";
+ my $weightdir="$dir/weights.pass$opt_iter/";
+ check_call("mkdir -p $logdir");
+ check_call("mkdir -p $weightdir");
+
+ #decode
+ print STDERR "RUNNING DECODER AT ";
+ print STDERR unchecked_output("date");
+# my $im1 = $opt_iter - 1;
+ my $weightsFile="$dir/weights.$opt_iter";
+ print "ITER $iteration " ;
+ my $cur_pass = "-p 0$opt_iter";
+ my $decoder_cmd = "$decoder -c $iniFile -w $weightsFile $refs_comma_sep -m $metric -s $metric_scale -a -b $update_size -k $kbest_size -o $optimizer $cur_pass -O $weightdir -D $dir -h $hopes -f $fears -C $step_size";
+ if($unique_kbest){
+ $decoder_cmd .= " -u";
+ }
+ if($latent){
+ $decoder_cmd .= " -l";
+ }
+ if($sample_max){
+ $decoder_cmd .= " -t $sample_max";
+ }
+ if ($density_prune) {
+ $decoder_cmd .= " --density_prune $density_prune";
+ }
+ my $pcmd;
+ if ($run_local) {
+ $pcmd = "cat $srcFile |";
+ } elsif ($use_make) {
+ # TODO: Throw error when decode_nodes is specified along with use_make
+ $pcmd = "cat $srcFile | $parallelize --use-fork -p $pmem -e $logdir -j $use_make --";
+ } elsif ($run_local_server){
+ $pcmd = "cat $srcFile | $local_server $usefork -p $pmem -e $logdir -n $decode_nodes --";
+ }
+ else {
+ $pcmd = "cat $srcFile | $parallelize $usefork -p $pmem -e $logdir -j $decode_nodes --baseport $portn --";
+ }
+ my $cmd = "$pcmd $decoder_cmd 2> $decoderLog 1> $runFile";
+ print STDERR "COMMAND:\n$cmd\n";
+ check_bash_call($cmd);
+
+ my $retries = 0;
+ my $num_topbest;
+ while($retries < 5) {
+ $num_topbest = check_output("wc -l < $runFile");
+ print STDERR "NUMBER OF TOP-BEST HYPs: $num_topbest\n";
+ if($devSize == $num_topbest) {
+ last;
+ } else {
+ print STDERR "Incorrect number of topbest. Waiting for distributed filesystem and retrying...\n";
+ sleep(3);
+ }
+ $retries++;
+ }
+ die "Dev set contains $devSize sentences, but we don't have topbest for all these! Decoder failure? Check $decoderLog\n" if ($devSize != $num_topbest);
+
+
+ #score the output from this iteration
+ open RUN, "<$runFile" or die "Can't read $runFile: $!";
+ open H, ">$runFile.H" or die;
+ open F, ">$runFile.F" or die;
+ open B, ">$runFile.B" or die;
+ while(<RUN>) {
+ chomp();
+ (my $hope,my $best,my $fear) = split(/ \|\|\| /);
+ print H "$hope \n";
+ print B "$best \n";
+ print F "$fear \n";
+ }
+ close RUN;
+ close F; close B; close H;
+
+ my $dec_score = check_output("cat $runFile.B | $SCORER $refs_comma_sep -l $metric");
+ my $dec_score_h = check_output("cat $runFile.H | $SCORER $refs_comma_sep -l $metric");
+ my $dec_score_f = check_output("cat $runFile.F | $SCORER $refs_comma_sep -l $metric");
+ chomp $dec_score; chomp $dec_score_h; chomp $dec_score_f;
+ print STDERR "DECODER SCORE: $dec_score HOPE: $dec_score_h FEAR: $dec_score_f\n";
+
+ # save space
+ check_call("gzip -f $runFile");
+ check_call("gzip -f $decoderLog");
+ my $iter_filler="";
+ if($opt_iter < 10)
+ {$iter_filler="0";}
+
+ my $nextIter = $opt_iter + 1;
+ my $newWeightsFile = "$dir/weights.$nextIter";
+ $lastWeightsFile = "$dir/weights.$opt_iter";
+
+ average_weights("$weightdir/weights.mira-pass*.*[0-9].gz", $newWeightsFile, $logdir);
+# check_call("cp $lastW $newWeightsFile");
+# if ($icc < 2) {
+# print STDERR "\nREACHED STOPPING CRITERION: score change too little\n";
+# last;
+# }
+ system("gzip -f $logdir/kbes*");
+ print STDERR "\n==========\n";
+ $iteration++;
+}
+#find
+#my $cmd = `grep SCORE /fs/clip-galep5/lexical_tm/log.runmira.nist.20 | cat -n | sort -k +2 | tail -1`;
+#$cmd =~ m/([0-9]+)/;
+#$lastWeightsFile = "$dir/weights.$1";
+#check_call("ln -s $lastWeightsFile $dir/weights.tuned");
+print STDERR "\nFINAL WEIGHTS: $lastWeightsFile\n(Use -w <this file> with the decoder)\n\n";
+
+print STDOUT "$lastWeightsFile\n";
+
+sub get_lines {
+ my $fn = shift @_;
+ open FL, "<$fn" or die "Couldn't read $fn: $!";
+ my $lc = 0;
+ while(<FL>) { $lc++; }
+ return $lc;
+}
+
+sub get_comma_sep_refs {
+ my ($r,$p) = @_;
+ my $o = check_output("echo $p");
+ chomp $o;
+ my @files = split /\s+/, $o;
+ return "-$r " . join(" -$r ", @files);
+}
+
+
+sub read_weights_file {
+ my ($file) = @_;
+ open F, "<$file" or die "Couldn't read $file: $!";
+ my @r = ();
+ my $pm = -1;
+ while(<F>) {
+ next if /^#/;
+ next if /^\s*$/;
+ chomp;
+ if (/^(.+)\s+(.+)$/) {
+ my $m = $1;
+ my $w = $2;
+ die "Weights out of order: $m <= $pm" unless $m > $pm;
+ push @r, $w;
+ } else {
+ warn "Unexpected feature name in weight file: $_";
+ }
+ }
+ close F;
+ return join ' ', @r;
+}
+
+sub write_config {
+ my $fh = shift;
+ my $cleanup = "yes";
+ if ($disable_clean) {$cleanup = "no";}
+
+ print $fh "\n";
+ print $fh "DECODER: $decoder\n";
+ print $fh "INI FILE: $iniFile\n";
+ print $fh "WORKING DIR: $dir\n";
+ print $fh "SOURCE (DEV): $srcFile\n";
+ print $fh "REFS (DEV): $refFiles\n";
+ print $fh "EVAL METRIC: $metric\n";
+ print $fh "START ITERATION: $iteration\n";
+ print $fh "MAX ITERATIONS: $max_iterations\n";
+ print $fh "DECODE NODES: $decode_nodes\n";
+ print $fh "HEAD NODE: $host\n";
+ print $fh "PMEM (DECODING): $pmem\n";
+ print $fh "CLEANUP: $cleanup\n";
+ print $fh "INITIAL WEIGHTS: $initialWeights\n";
+ print $fh "GRAMMAR PREFIX: $gpref\n";
+}
+
+sub update_weights_file {
+ my ($neww, $rfn, $rpts) = @_;
+ my @feats = @$rfn;
+ my @pts = @$rpts;
+ my $num_feats = scalar @feats;
+ my $num_pts = scalar @pts;
+ die "$num_feats (num_feats) != $num_pts (num_pts)" unless $num_feats == $num_pts;
+ open G, ">$neww" or die;
+ for (my $i = 0; $i < $num_feats; $i++) {
+ my $f = $feats[$i];
+ my $lambda = $pts[$i];
+ print G "$f $lambda\n";
+ }
+ close G;
+}
+
+sub enseg {
+ my $src = shift;
+ my $newsrc = shift;
+ my $grammarpref = shift;
+
+ open(SRC, $src);
+ open(NEWSRC, ">$newsrc");
+ my $i=0;
+ while (my $line=<SRC>){
+ chomp $line;
+ if ($line =~ /^\s*<seg/i) {
+ if($line =~ /id="[0-9]+"/) {
+ print NEWSRC "$line\n";
+ } else {
+ die "When using segments with pre-generated <seg> tags, you must include a zero-based id attribute";
+ }
+ }
+ elsif (defined $grammarpref) {
+ print NEWSRC "<seg id=\"$i\" grammar=\"$grammarpref.$i.gz\">$line</seg>\n";}
+ else {
+ print NEWSRC "<seg id=\"$i\">$line</seg>\n";
+ }
+ $i++;
+ }
+ close SRC;
+ close NEWSRC;
+}
+
+sub print_help {
+ print "Something wrong\n";
+}
+
+sub cmdline {
+ return join ' ',($0,@ORIG_ARGV);
+}
+
+#buggy: last arg gets quoted sometimes?
+my $is_shell_special=qr{[ \t\n\\><|&;"'`~*?{}$!()]};
+my $shell_escape_in_quote=qr{[\\"\$`!]};
+
+sub escape_shell {
+ my ($arg)=@_;
+ return undef unless defined $arg;
+ if ($arg =~ /$is_shell_special/) {
+ $arg =~ s/($shell_escape_in_quote)/\\$1/g;
+ return "\"$arg\"";
+ }
+ return $arg;
+}
+
+sub escaped_shell_args {
+ return map {local $_=$_;chomp;escape_shell($_)} @_;
+}
+
+sub escaped_shell_args_str {
+ return join ' ',&escaped_shell_args(@_);
+}
+
+sub escaped_cmdline {
+ return "$0 ".&escaped_shell_args_str(@ORIG_ARGV);
+}
+
+sub average_weights {
+
+ my $path = shift;
+ my $out = shift;
+ my $logpath = shift;
+ print "AVERAGE $path $out\n";
+ my %feature_weights= ();
+ my $total =0;
+ my $total_mult =0;
+ sleep(10);
+ foreach my $file (glob "$path")
+ {
+ $file =~ /\/([^\/]+).gz$/;
+ my $fname = $1;
+ my $cmd = "gzip -d $file";
+ $file =~ s/\.gz//;
+ check_bash_call($cmd);
+ my $mult = 0;
+ print "FILE $file \n";
+ open SCORE, "< $file" or next;
+ $total++;
+ while( <SCORE> ) {
+ my $line = $_;
+ if ($line !~ m/^\#/)
+ {
+ my @s = split(" ",$line);
+ $feature_weights{$s[0]}+= $mult * $s[1];
+ }
+ else
+ {
+ (my $msg,my $ran,$mult) = split(/ \|\|\| /);
+ print "RAN $ran $mult\n";
+ }
+ }
+ $total_mult += $mult;
+
+ close SCORE;
+ $cmd = "gzip $file"; check_bash_call($cmd);
+ }
+
+#print out new averaged weights
+ open OUT, "> $out" or next;
+ for my $f ( keys %feature_weights ) {
+ print "$f $feature_weights{$f} $total_mult\n";
+ my $ave = $feature_weights{$f} / $total_mult;
+
+ print "Printing $f $ave ||| ";
+ print OUT "$f $ave\n";
+ }
+
+}
--
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