adaptive hope-fear learner

mteval/ns.h

@@ -78,6 +78,7 @@ inline const SufficientStats operator-(const SufficientStats& a, const Sufficien
 struct SegmentEvaluator {
   virtual ~SegmentEvaluator();
   virtual void Evaluate(const std::vector<WordID>& hyp, SufficientStats* out) const = 0;
+  std::string src; // this may not always be available
 };
 
 // Instructions for implementing a new metric

mteval/ns_docscorer.cc

@@ -13,6 +13,40 @@ DocumentScorer::~DocumentScorer() {}
 
 DocumentScorer::DocumentScorer() {}
 
+DocumentScorer::DocumentScorer(const EvaluationMetric* metric,
+                               const string& src_ref_file) {
+  const WordID kDIV = TD::Convert("|||");
+  assert(!src_ref_file.empty());
+  cerr << "Loading source and references from " << src_ref_file << "...\n";
+  ReadFile rf(src_ref_file);
+  istream& in = *rf.stream();
+  unsigned lc = 0;
+  string src_ref;
+  vector<WordID> tmp;
+  vector<vector<WordID> > refs;
+  while(getline(in, src_ref)) {
+    ++lc;
+    size_t end_src = src_ref.find(" ||| ");
+    if (end_src == string::npos) {
+      cerr << "Expected SRC ||| REF [||| REF2 ||| REF3 ...] in line " << lc << endl;
+      abort();
+    }
+    refs.clear();
+    tmp.clear();
+    TD::ConvertSentence(src_ref, &tmp, end_src + 5);
+    unsigned last = 0;
+    for (unsigned j = 0; j < tmp.size(); ++j) {
+      if (tmp[j] == kDIV) {
+        refs.push_back(vector<WordID>(tmp.begin() + last, tmp.begin() + j));
+        last = j + 1;
+      }
+    }
+    refs.push_back(vector<WordID>(tmp.begin() + last, tmp.end()));
+    scorers_.push_back(metric->CreateSegmentEvaluator(refs));
+    scorers_.back()->src = src_ref.substr(0, end_src);
+  }
+}
+
 void DocumentScorer::Init(const EvaluationMetric* metric,
             const vector<string>& ref_files,
             const string& src_file,

mteval/scorer_test.cc

@@ -3,6 +3,7 @@
 #include <boost/test/unit_test.hpp>
 #include <boost/test/floating_point_comparison.hpp>
 
+#include "ns_docscorer.h"
 #include "ns.h"
 #include "tdict.h"
 #include "scorer.h"
@@ -223,4 +224,12 @@ BOOST_AUTO_TEST_CASE(NewScoreAPI) {
   //cerr << metric->ComputeScore(statse) << endl;
 }
 
+BOOST_AUTO_TEST_CASE(HybridSourceReferenceFileFormat) {
+  std::string path(boost::unit_test::framework::master_test_suite().argc == 2 ? boost::unit_test::framework::master_test_suite().argv[1] : TEST_DATA);
+  EvaluationMetric* metric = EvaluationMetric::Instance("IBM_BLEU");
+  DocumentScorer ds(metric, path + "/devset.txt");
+  BOOST_CHECK_EQUAL(2, ds.size());
+  BOOST_CHECK_EQUAL("Quelltext hier .", ds[0]->src);
+}
+
 BOOST_AUTO_TEST_SUITE_END()

mteval/test_data/devset.txt

@@ -0,0 +1,2 @@
+Quelltext hier . ||| source text here . ||| original text . ||| some source text .
+ein anderer Satz . ||| another sentence . ||| a different sentece .

training/mira/Makefile.am

@@ -1,15 +1,20 @@
-bin_PROGRAMS = kbest_mira \
-		kbest_cut_mira 
+bin_PROGRAMS = \
+  kbest_mira \
+  kbest_cut_mira \
+  ada_opt_sm
 
 EXTRA_DIST = mira.py
 
+ada_opt_sm_SOURCES = ada_opt_sm.cc
+ada_opt_sm_LDFLAGS= -rdynamic
+ada_opt_sm_LDADD = ../utils/libtraining_utils.a ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a
+
 kbest_mira_SOURCES = kbest_mira.cc
 kbest_mira_LDFLAGS= -rdynamic
 kbest_mira_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a
 
-
 kbest_cut_mira_SOURCES = kbest_cut_mira.cc
 kbest_cut_mira_LDFLAGS= -rdynamic
 kbest_cut_mira_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a
 
-AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I$(top_srcdir)/training/utils

training/mira/ada_opt_sm.cc

@@ -0,0 +1,198 @@
+#include "config.h"
+
+#include <boost/container/flat_map.hpp>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "filelib.h"
+#include "stringlib.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "candidate_set.h"
+#include "sentence_metadata.h"
+#include "ns.h"
+#include "ns_docscorer.h"
+#include "verbose.h"
+#include "hg.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "fdict.h"
+#include "sampler.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+boost::shared_ptr<MT19937> rng;
+vector<training::CandidateSet> kbests;
+SparseVector<weight_t> G, u, lambdas;
+double pseudo_doc_decay = 0.9;
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+    ("decoder_config,c",po::value<string>(),"[REQ] Decoder configuration file")
+    ("devset,d",po::value<string>(),"[REQ] Source/reference development set")
+    ("weights,w",po::value<string>(),"Initial feature weights file")
+    ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)")
+    ("size",po::value<unsigned>()->default_value(0), "Process rank (for multiprocess mode)")
+    ("rank",po::value<unsigned>()->default_value(1), "Number of processes (for multiprocess mode)")
+    ("optimizer,o",po::value<unsigned>()->default_value(1), "Optimizer (Adaptive MIRA=1)")
+    ("fear,f",po::value<unsigned>()->default_value(1), "Fear selection (model-cost=1, maxcost=2, maxscore=3)")
+    ("hope,h",po::value<unsigned>()->default_value(1), "Hope selection (model+cost=1, mincost=2)")
+    ("eta0", po::value<double>()->default_value(0.1), "Initial step size")
+    ("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), "Scale MT loss function by this amount")
+    ("pseudo_doc,e", "Use pseudo-documents for approximate scoring")
+    ("k_best_size,k", po::value<unsigned>()->default_value(500), "Size of hypothesis list to search for oracles");
+  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("decoder_config")
+          || !conf->count("devset")) {
+    cerr << dcmdline_options << endl;
+    return false;
+  }
+  return true;
+}
+
+struct TrainingObserver : public DecoderObserver {
+  explicit TrainingObserver(const EvaluationMetric& m, const int k) : metric(m), kbest_size(k), cur_eval() {}
+
+  const EvaluationMetric& metric;
+  const int kbest_size;
+  const SegmentEvaluator* cur_eval;
+  SufficientStats pdoc;
+  unsigned hi, vi, fi;  // hope, viterbi, fear
+
+  void SetSegmentEvaluator(const SegmentEvaluator* eval) {
+    cur_eval = eval;
+  }
+
+  virtual void NotifySourceParseFailure(const SentenceMetadata& smeta) {
+    cerr << "Failed to translate sentence with ID = " << smeta.GetSentenceID() << endl;
+    abort();
+  }
+
+  unsigned CostAugmentedDecode(const training::CandidateSet& cs,
+                               const SparseVector<double>& w,
+                               double alpha = 0) {
+    unsigned best_i = 0;
+    double best = -numeric_limits<double>::infinity();
+    for (unsigned i = 0; i < cs.size(); ++i) {
+      double s = cs[i].fmap.dot(w);
+      if (alpha)
+        s += alpha * metric.ComputeScore(cs[i].eval_feats + pdoc);
+      if (s > best) {
+        best = s;
+        best_i = i;
+      }
+    }
+    return best_i;
+  }
+
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+    pdoc *= pseudo_doc_decay;
+    const unsigned sent_id = smeta.GetSentenceID();
+    kbests[sent_id].AddUniqueKBestCandidates(*hg, kbest_size, cur_eval);
+    vi = CostAugmentedDecode(kbests[sent_id], lambdas);
+    hi = CostAugmentedDecode(kbests[sent_id], lambdas, 1.0);
+    fi = CostAugmentedDecode(kbests[sent_id], lambdas, -1.0);
+    cerr << sent_id << " ||| " << TD::GetString(kbests[sent_id][vi].ewords) << " ||| " << metric.ComputeScore(kbests[sent_id][vi].eval_feats + pdoc) << endl;
+    pdoc += kbests[sent_id][vi].eval_feats;  // update pseudodoc stats
+  }
+};
+
+int main(int argc, char** argv) {
+  SetSilent(true);  // turn off verbose decoder output
+  register_feature_functions();
+
+  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);
+
+  string metric_name = UppercaseString(conf["mt_metric"].as<string>());
+  if (metric_name == "COMBI") {
+    cerr << "WARNING: 'combi' metric is no longer supported, switching to 'COMB:TER=-0.5;IBM_BLEU=0.5'\n";
+    metric_name = "COMB:TER=-0.5;IBM_BLEU=0.5";
+  } else if (metric_name == "BLEU") {
+    cerr << "WARNING: 'BLEU' is ambiguous, assuming 'IBM_BLEU'\n";
+    metric_name = "IBM_BLEU";
+  }
+  EvaluationMetric* metric = EvaluationMetric::Instance(metric_name);
+  DocumentScorer ds(metric, conf["devset"].as<string>());
+  cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl;
+  kbests.resize(ds.size());
+  double eta = 0.001;
+
+  ReadFile ini_rf(conf["decoder_config"].as<string>());
+  Decoder decoder(ini_rf.stream());
+
+  vector<weight_t>& dense_weights = decoder.CurrentWeightVector();
+  if (conf.count("weights")) {
+    Weights::InitFromFile(conf["weights"].as<string>(), &dense_weights);
+    Weights::InitSparseVector(dense_weights, &lambdas);
+  }
+
+  TrainingObserver observer(*metric, conf["k_best_size"].as<unsigned>());
+
+  unsigned num = 200;
+  for (unsigned iter = 1; iter < num; ++iter) {
+    lambdas.init_vector(&dense_weights);
+    unsigned sent_id = rng->next() * ds.size();
+    cerr << "Learning from sentence id: " << sent_id << endl;
+    observer.SetSegmentEvaluator(ds[sent_id]);
+    decoder.SetId(sent_id);
+    decoder.Decode(ds[sent_id]->src, &observer);
+    if (observer.vi != observer.hi) {  // viterbi != hope
+      SparseVector<double> grad = kbests[sent_id][observer.fi].fmap;
+      grad -= kbests[sent_id][observer.hi].fmap;
+      cerr << "GRAD: " << grad << endl;
+      const SparseVector<double>& g = grad;
+#if HAVE_CXX11 && (__GNUC_MINOR__ > 4 || __GNUC__ > 4)
+      for (auto& gi : g) {
+#else
+      for (SparseVector<double>::const_iterator it = g.begin(); it != g.end(); ++it) {
+        const pair<unsigned,double>& gi = *it;
+#endif
+        if (gi.second) {
+          u[gi.first] += gi.second;
+          G[gi.first] += gi.second * gi.second;
+          lambdas.set_value(gi.first, 1.0);  // this is a dummy value to trigger recomputation
+        }
+      }
+      for (SparseVector<double>::iterator it = lambdas.begin(); it != lambdas.end(); ++it) {
+        const pair<unsigned,double>& xi = *it;
+        double z = fabs(u[xi.first] / iter) - 0.0;
+        double s = 1;
+        if (u[xi.first] > 0) s = -1;
+        if (z > 0 && G[xi.first]) {
+          lambdas.set_value(xi.first, eta * s * z * iter / sqrt(G[xi.first]));
+        } else {
+          lambdas.set_value(xi.first, 0.0);
+        }
+      }
+    }
+  }
+  cerr << "Optimization complete.\n";
+  Weights::WriteToFile("-", dense_weights, true);
+  return 0;
+}
+

training/utils/candidate_set.cc

@@ -171,4 +171,19 @@ void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, c
   Dedup();
 }
 
+void CandidateSet::AddUniqueKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer) {
+  typedef KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique> K;
+  K kbest(hg, kbest_size);
+
+  for (unsigned i = 0; i < kbest_size; ++i) {
+    const K::Derivation* d =
+      kbest.LazyKthBest(hg.nodes_.size() - 1, i);
+    if (!d) break;
+    cs.push_back(Candidate(d->yield, d->feature_values));
+    if (scorer)
+      scorer->Evaluate(d->yield, &cs.back().eval_feats);
+  }
+  Dedup();
+}
+
 }

training/utils/candidate_set.h

@@ -47,7 +47,7 @@ class CandidateSet {
   void ReadFromFile(const std::string& file);
   void WriteToFile(const std::string& file) const;
   void AddKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer = NULL);
-  // TODO add code to do unique k-best
+  void AddUniqueKBestCandidates(const Hypergraph& hg, size_t kbest_size, const SegmentEvaluator* scorer = NULL);
   // TODO add code to draw k samples
 
  private:

utils/stringlib.h

@@ -242,6 +242,21 @@ void VisitTokens(std::string const& s,F f) {
   VisitTokens(mp.p,mp.p+s.size(),f);
 }
 
+template <class F>
+void VisitTokens(std::string const& s,F f, unsigned start) {
+  if (0) {
+  std::vector<std::string> ss=SplitOnWhitespace(s);
+  for (unsigned i=0;i<ss.size();++i)
+    f(ss[i]);
+  return;
+  }
+  //FIXME:
+  if (s.empty()) return;
+  mutable_c_str mp(s);
+  SLIBDBG("mp="<<mp.p);
+  VisitTokens(mp.p+start,mp.p+s.size(),f);
+}
+
 inline void SplitCommandAndParam(const std::string& in, std::string* cmd, std::string* param) {
   cmd->clear();
   param->clear();

utils/tdict.cc

@@ -70,7 +70,7 @@ struct add_wordids {
 
 }
 
-void TD::ConvertSentence(std::string const& s, std::vector<WordID>* ids) {
+void TD::ConvertSentence(std::string const& s, std::vector<WordID>* ids, unsigned start) {
   ids->clear();
-  VisitTokens(s,add_wordids(ids));
+  VisitTokens(s,add_wordids(ids),start);
 }

utils/tdict.h

@@ -9,7 +9,7 @@
 
 struct TD {
   static WordID end(); // next id to be assigned; [begin,end) give the non-reserved tokens seen so far
-  static void ConvertSentence(std::string const& sent, std::vector<WordID>* ids);
+  static void ConvertSentence(std::string const& sent, std::vector<WordID>* ids, unsigned start=0);
   static void GetWordIDs(const std::vector<std::string>& strings, std::vector<WordID>* ids);
   static std::string GetString(const std::vector<WordID>& str);
   static std::string GetString(WordID const* i,WordID const* e);