summaryrefslogtreecommitdiff
path: root/gi/pyp-topics/src/train-contexts.cc
blob: d7262cdc17644aabf1a80ab553663e3e41707e28 (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
// STL
#include <iostream>
#include <fstream>
#include <algorithm>
#include <iterator>

// Boost
#include <boost/program_options/parsers.hpp>
#include <boost/program_options/variables_map.hpp>
#include <boost/scoped_ptr.hpp>

// Local
#include "pyp-topics.hh"
#include "corpus.hh"
#include "contexts_corpus.hh"
#include "gzstream.hh"
#include "mt19937ar.h"

static const char *REVISION = "$Rev$";

// Namespaces
using namespace boost;
using namespace boost::program_options;
using namespace std;

int main(int argc, char **argv)
{
  std::cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
  std::cout << REVISION << '\n' << std::endl;

  ////////////////////////////////////////////////////////////////////////////////////////////
  // Command line processing
  variables_map vm; 

  // Command line processing
  {
    options_description cmdline_options("Allowed options");
    cmdline_options.add_options()
      ("help,h", "print help message")
      ("data,d", value<string>(), "file containing the documents and context terms")
      ("topics,t", value<int>()->default_value(50), "number of topics")
      ("document-topics-out,o", value<string>(), "file to write the document topics to")
      ("default-topics-out", value<string>(), "file to write default term topic assignments.")
      ("topic-words-out,w", value<string>(), "file to write the topic word distribution to")
      ("samples,s", value<int>()->default_value(10), "number of sampling passes through the data")
      ("backoff-type", value<string>(), "backoff type: none|simple")
      ("filter-singleton-contexts", "filter singleton contexts")
      ("hierarchical-topics", "Use a backoff hierarchical PYP as the P0 for the document topics distribution.")
      ;
    store(parse_command_line(argc, argv, cmdline_options), vm); 
    notify(vm);

    if (vm.count("help")) { 
      cout << cmdline_options << "\n"; 
      return 1; 
    }
  }
  ////////////////////////////////////////////////////////////////////////////////////////////

  if (!vm.count("data")) {
    cerr << "Please specify a file containing the data." << endl;
    return 1;
  }

  // seed the random number generator
  //mt_init_genrand(time(0));

  PYPTopics model(vm["topics"].as<int>(), vm.count("hierarchical-topics"));

  // read the data
  BackoffGenerator* backoff_gen=0;
  if (vm.count("backoff-type")) {
    if (vm["backoff-type"].as<std::string>() == "none") {
      backoff_gen = 0;
    }
    else if (vm["backoff-type"].as<std::string>() == "simple") {
      backoff_gen = new SimpleBackoffGenerator();
    }
    else {
      std::cerr << "Backoff type (--backoff-type) must be one of none|simple." << std::endl;
      return(1);
    }
  }

  ContextsCorpus contexts_corpus;
  contexts_corpus.read_contexts(vm["data"].as<string>(), backoff_gen, vm.count("filter-singleton-contexts"));
  model.set_backoff(contexts_corpus.backoff_index());

  if (backoff_gen) 
    delete backoff_gen;

  // train the sampler
  model.sample(contexts_corpus, vm["samples"].as<int>());

  if (vm.count("document-topics-out")) {
    ogzstream documents_out(vm["document-topics-out"].as<string>().c_str());

    int document_id=0;
    std::set<int> all_terms;
    for (Corpus::const_iterator corpusIt=contexts_corpus.begin(); 
         corpusIt != contexts_corpus.end(); ++corpusIt, ++document_id) {
      std::vector<int> unique_terms;
      for (Document::const_iterator docIt=corpusIt->begin();
           docIt != corpusIt->end(); ++docIt) {
        if (unique_terms.empty() || *docIt != unique_terms.back())
          unique_terms.push_back(*docIt);
      }
      documents_out << contexts_corpus.key(document_id) << '\t';
      for (std::vector<int>::const_iterator termIt=unique_terms.begin();
           termIt != unique_terms.end(); ++termIt) {
        if (termIt != unique_terms.begin())
          documents_out << " ||| ";
        std::vector<std::string> strings = contexts_corpus.context2string(*termIt);
        std::copy(strings.begin(), strings.end(), std::ostream_iterator<std::string>(documents_out, " "));
        documents_out << "||| C=" << model.max(document_id, *termIt);

        all_terms.insert(*termIt);
      }
      documents_out << std::endl;
    }
    documents_out.close();

    std::ofstream default_topics(vm["default-topics-out"].as<string>().c_str());
    default_topics << model.max_topic() << std::endl;
    for (std::set<int>::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) {
      std::vector<std::string> strings = contexts_corpus.context2string(*termIt);
      default_topics << model.max(-1, *termIt) << " ||| ";
      std::copy(strings.begin(), strings.end(), std::ostream_iterator<std::string>(default_topics, " "));
      default_topics << std::endl;
    }
  }

  if (vm.count("topic-words-out")) {
    ogzstream topics_out(vm["topic-words-out"].as<string>().c_str());
    model.print_topic_terms(topics_out);
    topics_out.close();
  }

  std::cout << std::endl;

  return 0;
}