cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 428 deletions

diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
deleted file mode 100644
index 1d5126e4..00000000
--- a/gi/pf/learn_cfg.cc
+++ /dev/null
@@ -1,428 +0,0 @@
-#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;
-
-boost::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.num_tables());
-        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.num_tables());
-        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;
-}
-