summaryrefslogtreecommitdiff
path: root/gi/pf/learn_cfg.cc
blob: 44eaa1620973c978dfc3d5313c06a232b4ef2307 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
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;

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.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;
}