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
|
// 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>
#include <boost/mpi/environment.hpp>
#include <boost/mpi/communicator.hpp>
#include <boost/lexical_cast.hpp>
// Local
#include "mpi-pyp-topics.hh"
#include "corpus.hh"
#include "mpi-corpus.hh"
#include "gzstream.hh"
static const char *REVISION = "$Rev: 170 $";
// Namespaces
using namespace boost;
using namespace boost::program_options;
using namespace std;
int main(int argc, char **argv)
{
mpi::environment env(argc, argv);
mpi::communicator world;
int rank = world.rank();
bool am_root = (rank==0);
if (am_root) cout << "Pitman Yor topic models: Copyright 2010 Phil Blunsom\n";
if (am_root) std::cout << "I am process " << world.rank() << " of " << world.size() << "." << std::endl;
if (am_root) cout << REVISION << '\n' <<endl;
////////////////////////////////////////////////////////////////////////////////////////////
// Command line processing
variables_map vm;
// Command line processing
{
options_description cmdline_specific("Command line specific options");
cmdline_specific.add_options()
("help,h", "print help message")
("config,c", value<string>(), "config file specifying additional command line options")
;
options_description config_options("Allowed options");
config_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.")
("binary-counts,b", "Use binary rather than integer counts for contexts.")
("freq-cutoff-start", value<int>()->default_value(0), "initial frequency cutoff.")
("freq-cutoff-end", value<int>()->default_value(0), "final frequency cutoff.")
("freq-cutoff-interval", value<int>()->default_value(0), "number of iterations between frequency decrement.")
("max-contexts-per-document", value<int>()->default_value(0), "Only sample the n most frequent contexts for a document.")
;
cmdline_specific.add(config_options);
store(parse_command_line(argc, argv, cmdline_specific), vm);
notify(vm);
if (vm.count("config") > 0) {
ifstream config(vm["config"].as<string>().c_str());
store(parse_config_file(config, config_options), vm);
}
if (vm.count("help")) {
cout << cmdline_specific << "\n";
return 1;
}
}
////////////////////////////////////////////////////////////////////////////////////////////
if (!vm.count("data")) {
cerr << "Please specify a file containing the data." << endl;
return 1;
}
// seed the random number generator: 0 = automatic, specify value otherwise
unsigned long seed = 0;
MPIPYPTopics model(vm["topics"].as<int>(), vm.count("hierarchical-topics"), seed);
// 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 {
cerr << "Backoff type (--backoff-type) must be one of none|simple." <<endl;
return(1);
}
}
//ContextsCorpus contexts_corpus;
MPICorpus contexts_corpus;
contexts_corpus.read_contexts(vm["data"].as<string>(), backoff_gen, /*vm.count("filter-singleton-contexts")*/ false, vm.count("binary-counts"));
int mpi_start = 0, mpi_end = 0;
contexts_corpus.bounds(&mpi_start, &mpi_end);
std::cerr << "\tProcess " << rank << " has documents " << mpi_start << " -> " << mpi_end << "." << std::endl;
model.set_backoff(contexts_corpus.backoff_index());
if (backoff_gen)
delete backoff_gen;
// train the sampler
model.sample_corpus(contexts_corpus, vm["samples"].as<int>(),
vm["freq-cutoff-start"].as<int>(),
vm["freq-cutoff-end"].as<int>(),
vm["freq-cutoff-interval"].as<int>(),
vm["max-contexts-per-document"].as<int>());
if (vm.count("document-topics-out")) {
std::ofstream documents_out((vm["document-topics-out"].as<string>() + ".pyp-process-" + boost::lexical_cast<std::string>(rank)).c_str());
//int documents = contexts_corpus.num_documents();
/*
int mpi_start = 0, mpi_end = documents;
if (world.size() != 1) {
mpi_start = (documents / world.size()) * rank;
if (rank == world.size()-1) mpi_end = documents;
else mpi_end = (documents / world.size())*(rank+1);
}
*/
map<int,int> all_terms;
for (int document_id=mpi_start; document_id<mpi_end; ++document_id) {
assert (document_id < contexts_corpus.num_documents());
const Document& doc = contexts_corpus.at(document_id);
vector<int> unique_terms;
for (Document::const_iterator docIt=doc.begin(); docIt != doc.end(); ++docIt) {
if (unique_terms.empty() || *docIt != unique_terms.back())
unique_terms.push_back(*docIt);
// increment this terms frequency
pair<map<int,int>::iterator,bool> insert_result = all_terms.insert(make_pair(*docIt,1));
if (!insert_result.second)
all_terms[*docIt] = all_terms[*docIt] + 1;
}
documents_out << contexts_corpus.key(document_id) << '\t';
documents_out << model.max(document_id).first << " " << doc.size() << " ||| ";
for (std::vector<int>::const_iterator termIt=unique_terms.begin(); termIt != unique_terms.end(); ++termIt) {
if (termIt != unique_terms.begin())
documents_out << " ||| ";
vector<std::string> strings = contexts_corpus.context2string(*termIt);
copy(strings.begin(), strings.end(),ostream_iterator<std::string>(documents_out, " "));
std::pair<int,MPIPYPTopics::F> maxinfo = model.max(document_id, *termIt);
documents_out << "||| C=" << maxinfo.first << " P=" << maxinfo.second;
}
documents_out <<endl;
}
documents_out.close();
world.barrier();
if (am_root) {
ogzstream root_documents_out(vm["document-topics-out"].as<string>().c_str());
for (int p=0; p < world.size(); ++p) {
std::string rank_p_prefix((vm["document-topics-out"].as<string>() + ".pyp-process-" + boost::lexical_cast<std::string>(p)).c_str());
std::ifstream rank_p_trees_istream(rank_p_prefix.c_str(), std::ios_base::binary);
root_documents_out << rank_p_trees_istream.rdbuf();
rank_p_trees_istream.close();
remove((rank_p_prefix).c_str());
}
root_documents_out.close();
}
if (am_root && vm.count("default-topics-out")) {
ofstream default_topics(vm["default-topics-out"].as<string>().c_str());
default_topics << model.max_topic() <<endl;
for (std::map<int,int>::const_iterator termIt=all_terms.begin(); termIt != all_terms.end(); ++termIt) {
vector<std::string> strings = contexts_corpus.context2string(termIt->first);
default_topics << model.max(-1, termIt->first).first << " ||| " << termIt->second << " ||| ";
copy(strings.begin(), strings.end(),ostream_iterator<std::string>(default_topics, " "));
default_topics <<endl;
}
}
}
if (am_root && 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();
}
cout <<endl;
return 0;
}
|
