merge with upstream

1 files changed, 428 insertions, 0 deletions

diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
new file mode 100644
index 00000000..ed1772bf
--- /dev/null
+++ b/gi/pf/learn_cfg.cc
@@ -0,0 +1,428 @@
+#include <iostream>
+#include <tr1/memory>
+#include <queue>
+
+#include <boost/functional.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "inside_outside.h"
+#include "hg.h"
+#include "bottom_up_parser.h"
+#include "fdict.h"
+#include "grammar.h"
+#include "m.h"
+#include "trule.h"
+#include "tdict.h"
+#include "filelib.h"
+#include "dict.h"
+#include "sampler.h"
+#include "ccrp.h"
+#include "ccrp_onetable.h"
+
+using namespace std;
+using namespace tr1;
+namespace po = boost::program_options;
+
+shared_ptr<MT19937> prng;
+vector<int> nt_vocab;
+vector<int> nt_id_to_index;
+static unsigned kMAX_RULE_SIZE = 0;
+static unsigned kMAX_ARITY = 0;
+static bool kALLOW_MIXED = true;  // allow rules with mixed terminals and NTs
+static bool kHIERARCHICAL_PRIOR = false;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
+        ("input,i",po::value<string>(),"Read parallel data from")
+        ("max_rule_size,m", po::value<unsigned>()->default_value(0), "Maximum rule size (0 for unlimited)")
+        ("max_arity,a", po::value<unsigned>()->default_value(0), "Maximum number of nonterminals in a rule (0 for unlimited)")
+        ("no_mixed_rules,M", "Do not mix terminals and nonterminals in a rule RHS")
+        ("nonterminals,n", po::value<unsigned>()->default_value(1), "Size of nonterminal vocabulary")
+        ("hierarchical_prior,h", "Use hierarchical prior")
+        ("random_seed,S",po::value<uint32_t>(), "Random seed");
+  po::options_description clo("Command line options");
+  clo.add_options()
+        ("config", po::value<string>(), "Configuration file")
+        ("help", "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") == 0)) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+unsigned ReadCorpus(const string& filename,
+                    vector<vector<WordID> >* e,
+                    set<WordID>* vocab_e) {
+  e->clear();
+  vocab_e->clear();
+  istream* in;
+  if (filename == "-")
+    in = &cin;
+  else
+    in = new ifstream(filename.c_str());
+  assert(*in);
+  string line;
+  unsigned toks = 0;
+  while(*in) {
+    getline(*in, line);
+    if (line.empty() && !*in) break;
+    e->push_back(vector<int>());
+    vector<int>& le = e->back();
+    TD::ConvertSentence(line, &le);
+    for (unsigned i = 0; i < le.size(); ++i)
+      vocab_e->insert(le[i]);
+    toks += le.size();
+  }
+  if (in != &cin) delete in;
+  return toks;
+}
+
+struct Grid {
+  // a b c d e
+  // 0 - 0 - -
+  vector<int> grid;
+};
+
+struct BaseRuleModel {
+  explicit BaseRuleModel(unsigned term_size,
+                         unsigned nonterm_size = 1) :
+      unif_term(1.0 / term_size),
+      unif_nonterm(1.0 / nonterm_size) {}
+  prob_t operator()(const TRule& r) const {
+    prob_t p; p.logeq(Md::log_poisson(1.0, r.f_.size()));
+    const prob_t term_prob((2.0 + 0.01*r.f_.size()) / (r.f_.size() + 2));
+    const prob_t nonterm_prob(1.0 - term_prob.as_float());
+    for (unsigned i = 0; i < r.f_.size(); ++i) {
+      if (r.f_[i] <= 0) {     // nonterminal
+        if (kALLOW_MIXED) p *= nonterm_prob;
+        p *= unif_nonterm;
+      } else {                // terminal
+        if (kALLOW_MIXED) p *= term_prob;
+        p *= unif_term;
+      }
+    }
+    return p;
+  }
+  const prob_t unif_term, unif_nonterm;
+};
+
+struct HieroLMModel {
+  explicit HieroLMModel(unsigned vocab_size, unsigned num_nts = 1) :
+      base(vocab_size, num_nts),
+      q0(1,1,1,1),
+      nts(num_nts, CCRP<TRule>(1,1,1,1)) {}
+
+  prob_t Prob(const TRule& r) const {
+    return nts[nt_id_to_index[-r.lhs_]].prob(r, p0(r));
+  }
+
+  inline prob_t p0(const TRule& r) const {
+    if (kHIERARCHICAL_PRIOR)
+      return q0.prob(r, base(r));
+    else
+      return base(r);
+  }
+
+  int Increment(const TRule& r, MT19937* rng) {
+    const int delta = nts[nt_id_to_index[-r.lhs_]].increment(r, p0(r), rng);
+    if (kHIERARCHICAL_PRIOR && delta)
+      q0.increment(r, base(r), rng);
+    return delta;
+    // return x.increment(r);
+  }
+
+  int Decrement(const TRule& r, MT19937* rng) {
+    const int delta = nts[nt_id_to_index[-r.lhs_]].decrement(r, rng);
+    if (kHIERARCHICAL_PRIOR && delta)
+      q0.decrement(r, rng);
+    return delta;
+    //return x.decrement(r);
+  }
+
+  prob_t Likelihood() const {
+    prob_t p = prob_t::One();
+    for (unsigned i = 0; i < nts.size(); ++i) {
+      prob_t q; q.logeq(nts[i].log_crp_prob());
+      p *= q;
+      for (CCRP<TRule>::const_iterator it = nts[i].begin(); it != nts[i].end(); ++it) {
+        prob_t tp = p0(it->first);
+        tp.poweq(it->second.table_counts_.size());
+        p *= tp;
+      }
+    }
+    if (kHIERARCHICAL_PRIOR) {
+      prob_t q; q.logeq(q0.log_crp_prob());
+      p *= q;
+      for (CCRP<TRule>::const_iterator it = q0.begin(); it != q0.end(); ++it) {
+        prob_t tp = base(it->first);
+        tp.poweq(it->second.table_counts_.size());
+        p *= tp;
+      }
+    }
+    //for (CCRP_OneTable<TRule>::const_iterator it = x.begin(); it != x.end(); ++it)
+    //    p *= base(it->first);
+    return p;
+  }
+
+  void ResampleHyperparameters(MT19937* rng) {
+    for (unsigned i = 0; i < nts.size(); ++i)
+      nts[i].resample_hyperparameters(rng);
+    if (kHIERARCHICAL_PRIOR) {
+      q0.resample_hyperparameters(rng);
+      cerr << "[base d=" << q0.discount() << ", s=" << q0.strength() << "]";
+    }
+    cerr << " d=" << nts[0].discount() << ", s=" << nts[0].strength() << endl;
+  }
+
+  const BaseRuleModel base;
+  CCRP<TRule> q0;
+  vector<CCRP<TRule> > nts;
+  //CCRP_OneTable<TRule> x;
+};
+
+vector<GrammarIter* > tofreelist;
+
+HieroLMModel* plm;
+
+struct NPGrammarIter : public GrammarIter, public RuleBin {
+  NPGrammarIter() : arity() { tofreelist.push_back(this); }
+  NPGrammarIter(const TRulePtr& inr, const int a, int symbol) : arity(a) {
+    if (inr) {
+      r.reset(new TRule(*inr));
+    } else {
+      r.reset(new TRule);
+    }
+    TRule& rr = *r;
+    rr.lhs_ = nt_vocab[0];
+    rr.f_.push_back(symbol);
+    rr.e_.push_back(symbol < 0 ? (1-int(arity)) : symbol);
+    tofreelist.push_back(this);
+  }
+  inline static unsigned NextArity(int cur_a, int symbol) {
+    return cur_a + (symbol <= 0 ? 1 : 0);
+  }
+  virtual int GetNumRules() const {
+    if (r) return nt_vocab.size(); else return 0;
+  }
+  virtual TRulePtr GetIthRule(int i) const {
+    if (i == 0) return r;
+    TRulePtr nr(new TRule(*r));
+    nr->lhs_ = nt_vocab[i];
+    return nr;
+  }
+  virtual int Arity() const {
+    return arity;
+  }
+  virtual const RuleBin* GetRules() const {
+    if (!r) return NULL; else return this;
+  }
+  virtual const GrammarIter* Extend(int symbol) const {
+    const int next_arity = NextArity(arity, symbol);
+    if (kMAX_ARITY && next_arity > kMAX_ARITY)
+      return NULL;
+    if (!kALLOW_MIXED && r) {
+      bool t1 = r->f_.front() <= 0;
+      bool t2 = symbol <= 0;
+      if (t1 != t2) return NULL;
+    }
+    if (!kMAX_RULE_SIZE || !r || (r->f_.size() < kMAX_RULE_SIZE))
+      return new NPGrammarIter(r, next_arity, symbol);
+    else
+      return NULL;
+  }
+  const unsigned char arity;
+  TRulePtr r;
+};
+
+struct NPGrammar : public Grammar {
+  virtual const GrammarIter* GetRoot() const {
+    return new NPGrammarIter;
+  }
+};
+
+prob_t TotalProb(const Hypergraph& hg) {
+  return Inside<prob_t, EdgeProb>(hg);
+}
+
+void SampleDerivation(const Hypergraph& hg, MT19937* rng, vector<unsigned>* sampled_deriv) {
+  vector<prob_t> node_probs;
+  Inside<prob_t, EdgeProb>(hg, &node_probs);
+  queue<unsigned> q;
+  q.push(hg.nodes_.size() - 2);
+  while(!q.empty()) {
+    unsigned cur_node_id = q.front();
+//    cerr << "NODE=" << cur_node_id << endl;
+    q.pop();
+    const Hypergraph::Node& node = hg.nodes_[cur_node_id];
+    const unsigned num_in_edges = node.in_edges_.size();
+    unsigned sampled_edge = 0;
+    if (num_in_edges == 1) {
+      sampled_edge = node.in_edges_[0];
+    } else {
+      //prob_t z;
+      assert(num_in_edges > 1);
+      SampleSet<prob_t> ss;
+      for (unsigned j = 0; j < num_in_edges; ++j) {
+        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
+        prob_t p = edge.edge_prob_;
+        for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
+          p *= node_probs[edge.tail_nodes_[k]];
+        ss.add(p);
+//        cerr << log(ss[j]) << " ||| " << edge.rule_->AsString() << endl;
+        //z += p;
+      }
+//      for (unsigned j = 0; j < num_in_edges; ++j) {
+//        const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
+//        cerr << exp(log(ss[j] / z)) << " ||| " << edge.rule_->AsString() << endl;
+//      }
+//      cerr << " --- \n";
+      sampled_edge = node.in_edges_[rng->SelectSample(ss)];
+    }
+    sampled_deriv->push_back(sampled_edge);
+    const Hypergraph::Edge& edge = hg.edges_[sampled_edge];
+    for (unsigned j = 0; j < edge.tail_nodes_.size(); ++j) {
+      q.push(edge.tail_nodes_[j]);
+    }
+  }
+  for (unsigned i = 0; i < sampled_deriv->size(); ++i) {
+    cerr << *hg.edges_[(*sampled_deriv)[i]].rule_ << endl;
+  }
+}
+
+void IncrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, HieroLMModel* plm, MT19937* rng) {
+  for (unsigned i = 0; i < d.size(); ++i)
+    plm->Increment(*hg.edges_[d[i]].rule_, rng);
+}
+
+void DecrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, HieroLMModel* plm, MT19937* rng) {
+  for (unsigned i = 0; i < d.size(); ++i)
+    plm->Decrement(*hg.edges_[d[i]].rule_, rng);
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+
+  InitCommandLine(argc, argv, &conf);
+  nt_vocab.resize(conf["nonterminals"].as<unsigned>());
+  assert(nt_vocab.size() > 0);
+  assert(nt_vocab.size() < 26);
+  {
+    string nt = "X";
+    for (unsigned i = 0; i < nt_vocab.size(); ++i) {
+      if (nt_vocab.size() > 1) nt[0] = ('A' + i);
+      int pid = TD::Convert(nt);
+      nt_vocab[i] = -pid;
+      if (pid >= nt_id_to_index.size()) {
+        nt_id_to_index.resize(pid + 1, -1);
+      }
+      nt_id_to_index[pid] = i;
+    }
+  }
+  vector<GrammarPtr> grammars;
+  grammars.push_back(GrammarPtr(new NPGrammar));
+
+  const unsigned samples = conf["samples"].as<unsigned>();
+  kMAX_RULE_SIZE = conf["max_rule_size"].as<unsigned>();
+  if (kMAX_RULE_SIZE == 1) {
+    cerr << "Invalid maximum rule size: must be 0 or >1\n";
+    return 1;
+  }
+  kMAX_ARITY = conf["max_arity"].as<unsigned>();
+  if (kMAX_ARITY == 1) {
+    cerr << "Invalid maximum arity: must be 0 or >1\n";
+    return 1;
+  }
+  kALLOW_MIXED = !conf.count("no_mixed_rules");
+
+  kHIERARCHICAL_PRIOR = conf.count("hierarchical_prior");
+
+  if (conf.count("random_seed"))
+    prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    prng.reset(new MT19937);
+  MT19937& rng = *prng;
+  vector<vector<WordID> > corpuse;
+  set<WordID> vocabe;
+  cerr << "Reading corpus...\n";
+  const unsigned toks = ReadCorpus(conf["input"].as<string>(), &corpuse, &vocabe);
+  cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
+  HieroLMModel lm(vocabe.size(), nt_vocab.size());
+
+  plm = &lm;
+  ExhaustiveBottomUpParser parser(TD::Convert(-nt_vocab[0]), grammars);
+
+  Hypergraph hg;
+  const int kGoal = -TD::Convert("Goal");
+  const int kLP = FD::Convert("LogProb");
+  SparseVector<double> v; v.set_value(kLP, 1.0);
+  vector<vector<unsigned> > derivs(corpuse.size());
+  vector<Lattice> cl(corpuse.size());
+  for (int ci = 0; ci < corpuse.size(); ++ci) {
+    vector<int>& src = corpuse[ci];
+    Lattice& lat = cl[ci];
+    lat.resize(src.size());
+    for (unsigned i = 0; i < src.size(); ++i)
+      lat[i].push_back(LatticeArc(src[i], 0.0, 1));
+  }
+  for (int SS=0; SS < samples; ++SS) {
+    const bool is_last = ((samples - 1) == SS);
+    prob_t dlh = prob_t::One();
+    for (int ci = 0; ci < corpuse.size(); ++ci) {
+      const vector<int>& src = corpuse[ci];
+      const Lattice& lat = cl[ci];
+      cerr << TD::GetString(src) << endl;
+      hg.clear();
+      parser.Parse(lat, &hg);  // exhaustive parse
+      vector<unsigned>& d = derivs[ci];
+      if (!is_last) DecrementDerivation(hg, d, &lm, &rng);
+      for (unsigned i = 0; i < hg.edges_.size(); ++i) {
+        TRule& r = *hg.edges_[i].rule_;
+        if (r.lhs_ == kGoal)
+          hg.edges_[i].edge_prob_ = prob_t::One();
+        else
+          hg.edges_[i].edge_prob_ = lm.Prob(r);
+      }
+      if (!is_last) {
+        d.clear();
+        SampleDerivation(hg, &rng, &d);
+        IncrementDerivation(hg, derivs[ci], &lm, &rng);
+      } else {
+        prob_t p = TotalProb(hg);
+        dlh *= p;
+        cerr << " p(sentence) = " << log(p) << "\t" << log(dlh) << endl;
+      }
+      if (tofreelist.size() > 200000) {
+        cerr << "Freeing ... ";
+        for (unsigned i = 0; i < tofreelist.size(); ++i)
+          delete tofreelist[i];
+        tofreelist.clear();
+        cerr << "Freed.\n";
+      }
+    }
+    double llh = log(lm.Likelihood());
+    cerr << "LLH=" << llh << "\tENTROPY=" << (-llh / log(2) / toks) << "\tPPL=" << pow(2, -llh / log(2) / toks) << endl;
+    if (SS % 10 == 9) lm.ResampleHyperparameters(&rng);
+    if (is_last) {
+      double z = log(dlh);
+      cerr << "TOTAL_PROB=" << z << "\tENTROPY=" << (-z / log(2) / toks) << "\tPPL=" << pow(2, -z / log(2) / toks) << endl;
+    }
+  }
+  for (unsigned i = 0; i < nt_vocab.size(); ++i)
+    cerr << lm.nts[i] << endl;
+  return 0;
+}
+