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
|
#include <iostream>
#include <tr1/memory>
#include <queue>
#include <boost/functional.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "corpus_tools.h"
#include "m.h"
#include "tdict.h"
#include "sampler.h"
#include "ccrp.h"
// A not very memory-efficient implementation of an 1-gram LM based on PYPs
// as described in Y.-W. Teh. (2006) A Hierarchical Bayesian Language Model
// based on Pitman-Yor Processes. In Proc. ACL.
using namespace std;
using namespace tr1;
namespace po = boost::program_options;
boost::shared_ptr<MT19937> prng;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("samples,n",po::value<unsigned>()->default_value(50),"Number of samples")
("train,i",po::value<string>(),"Training data file")
("test,T",po::value<string>(),"Test data file")
("discount_prior_a,a",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): a=this")
("discount_prior_b,b",po::value<double>()->default_value(1.0), "discount ~ Beta(a,b): b=this")
("strength_prior_s,s",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): s=this")
("strength_prior_r,r",po::value<double>()->default_value(1.0), "strength ~ Gamma(s,r): r=this")
("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("train") == 0)) {
cerr << dcmdline_options << endl;
exit(1);
}
}
// uniform base distribution (0-gram model)
struct UniformWordModel {
explicit UniformWordModel(unsigned vocab_size) : p0(1.0 / vocab_size), draws() {}
void increment() { ++draws; }
void decrement() { --draws; assert(draws >= 0); }
double prob(WordID) const { return p0; } // all words have equal prob
double log_likelihood() const { return draws * log(p0); }
const double p0;
int draws;
};
// represents an Unigram LM
struct UnigramLM {
UnigramLM(unsigned vs, double da, double db, double ss, double sr) :
uniform_vocab(vs),
crp(da, db, ss, sr, 0.8, 1.0) {}
void increment(WordID w, MT19937* rng) {
const double backoff = uniform_vocab.prob(w);
if (crp.increment(w, backoff, rng))
uniform_vocab.increment();
}
void decrement(WordID w, MT19937* rng) {
if (crp.decrement(w, rng))
uniform_vocab.decrement();
}
double prob(WordID w) const {
const double backoff = uniform_vocab.prob(w);
return crp.prob(w, backoff);
}
double log_likelihood() const {
double llh = uniform_vocab.log_likelihood();
llh += crp.log_crp_prob();
return llh;
}
void resample_hyperparameters(MT19937* rng) {
crp.resample_hyperparameters(rng);
}
double discount_a, discount_b, strength_s, strength_r;
double d, strength;
UniformWordModel uniform_vocab;
CCRP<WordID> crp;
};
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const unsigned samples = conf["samples"].as<unsigned>();
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;
const WordID kEOS = TD::Convert("</s>");
cerr << "Reading corpus...\n";
CorpusTools::ReadFromFile(conf["train"].as<string>(), &corpuse, &vocabe);
cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
vector<vector<WordID> > test;
if (conf.count("test"))
CorpusTools::ReadFromFile(conf["test"].as<string>(), &test);
else
test = corpuse;
UnigramLM lm(vocabe.size(),
conf["discount_prior_a"].as<double>(),
conf["discount_prior_b"].as<double>(),
conf["strength_prior_s"].as<double>(),
conf["strength_prior_r"].as<double>());
for (int SS=0; SS < samples; ++SS) {
for (int ci = 0; ci < corpuse.size(); ++ci) {
const vector<WordID>& s = corpuse[ci];
for (int i = 0; i <= s.size(); ++i) {
WordID w = (i < s.size() ? s[i] : kEOS);
if (SS > 0) lm.decrement(w, &rng);
lm.increment(w, &rng);
}
if (SS > 0) lm.decrement(kEOS, &rng);
lm.increment(kEOS, &rng);
}
cerr << "LLH=" << lm.log_likelihood() << endl;
//if (SS % 10 == 9) lm.resample_hyperparameters(&rng);
}
double llh = 0;
unsigned cnt = 0;
unsigned oovs = 0;
for (int ci = 0; ci < test.size(); ++ci) {
const vector<WordID>& s = test[ci];
for (int i = 0; i <= s.size(); ++i) {
WordID w = (i < s.size() ? s[i] : kEOS);
double lp = log(lm.prob(w)) / log(2);
if (i < s.size() && vocabe.count(w) == 0) {
cerr << "**OOV ";
++oovs;
lp = 0;
}
cerr << "p(" << TD::Convert(w) << ") = " << lp << endl;
llh -= lp;
cnt++;
}
}
cerr << " Log_10 prob: " << (-llh * log(2) / log(10)) << endl;
cerr << " Count: " << cnt << endl;
cerr << " OOVs: " << oovs << endl;
cerr << "Cross-entropy: " << (llh / cnt) << endl;
cerr << " Perplexity: " << pow(2, llh / cnt) << endl;
return 0;
}
|
