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#include <iostream>
#include <cmath>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "lattice.h"
#include "stringlib.h"
#include "filelib.h"
#include "ttables.h"
#include "tdict.h"
#include "em_utils.h"
namespace po = boost::program_options;
using namespace std;
bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("iterations,i",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
("beam_threshold,t",po::value<double>()->default_value(-4),"log_10 of beam threshold (-10000 to include everything, 0 max)")
("no_null_word,N","Do not generate from the null token")
("variational_bayes,v","Add a symmetric Dirichlet prior and infer VB estimate of weights")
("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior")
("no_add_viterbi,V","Do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
("help,h", "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 (argc < 2 || conf->count("help")) {
cerr << "Usage " << argv[0] << " [OPTIONS] corpus.fr-en\n";
cerr << dcmdline_options << endl;
return false;
}
return true;
}
int main(int argc, char** argv) {
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
const string fname = argv[argc - 1];
const int ITERATIONS = conf["iterations"].as<unsigned>();
const double BEAM_THRESHOLD = pow(10.0, conf["beam_threshold"].as<double>());
const bool use_null = (conf.count("no_null_word") == 0);
const WordID kNULL = TD::Convert("<eps>");
const bool add_viterbi = (conf.count("no_add_viterbi") == 0);
const bool variational_bayes = (conf.count("variational_bayes") > 0);
const double alpha = conf["alpha"].as<double>();
if (variational_bayes && alpha <= 0.0) {
cerr << "--alpha must be > 0\n";
return 1;
}
TTable tt;
TTable::Word2Word2Double was_viterbi;
for (int iter = 0; iter < ITERATIONS; ++iter) {
const bool final_iteration = (iter == (ITERATIONS - 1));
cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl;
ReadFile rf(fname);
istream& in = *rf.stream();
double likelihood = 0;
double denom = 0.0;
int lc = 0;
bool flag = false;
string line;
while(true) {
getline(in, line);
if (!in) break;
++lc;
if (lc % 1000 == 0) { cerr << '.'; flag = true; }
if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; }
string ssrc, strg;
ParseTranslatorInput(line, &ssrc, &strg);
Lattice src, trg;
LatticeTools::ConvertTextToLattice(ssrc, &src);
LatticeTools::ConvertTextToLattice(strg, &trg);
if (src.size() == 0 || trg.size() == 0) {
cerr << "Error: " << lc << "\n" << line << endl;
assert(src.size() > 0);
assert(trg.size() > 0);
}
denom += trg.size();
vector<double> probs(src.size() + 1);
const double src_logprob = -log(src.size() + 1);
for (int j = 0; j < trg.size(); ++j) {
const WordID& f_j = trg[j][0].label;
double sum = 0;
if (use_null) {
probs[0] = tt.prob(kNULL, f_j);
sum += probs[0];
}
for (int i = 1; i <= src.size(); ++i) {
probs[i] = tt.prob(src[i-1][0].label, f_j);
sum += probs[i];
}
if (final_iteration) {
if (add_viterbi) {
WordID max_i = 0;
double max_p = -1;
if (use_null) {
max_i = kNULL;
max_p = probs[0];
}
for (int i = 1; i <= src.size(); ++i) {
if (probs[i] > max_p) {
max_p = probs[i];
max_i = src[i-1][0].label;
}
}
was_viterbi[max_i][f_j] = 1.0;
}
} else {
if (use_null)
tt.Increment(kNULL, f_j, probs[0] / sum);
for (int i = 1; i <= src.size(); ++i)
tt.Increment(src[i-1][0].label, f_j, probs[i] / sum);
}
likelihood += log(sum) + src_logprob;
}
}
// log(e) = 1.0
double base2_likelihood = likelihood / log(2);
if (flag) { cerr << endl; }
cerr << " log_e likelihood: " << likelihood << endl;
cerr << " log_2 likelihood: " << base2_likelihood << endl;
cerr << " cross entropy: " << (-base2_likelihood / denom) << endl;
cerr << " perplexity: " << pow(2.0, -base2_likelihood / denom) << endl;
if (!final_iteration) {
if (variational_bayes)
tt.NormalizeVB(alpha);
else
tt.Normalize();
}
}
for (TTable::Word2Word2Double::iterator ei = tt.ttable.begin(); ei != tt.ttable.end(); ++ei) {
const TTable::Word2Double& cpd = ei->second;
const TTable::Word2Double& vit = was_viterbi[ei->first];
const string& esym = TD::Convert(ei->first);
double max_p = -1;
for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi)
if (fi->second > max_p) max_p = fi->second;
const double threshold = max_p * BEAM_THRESHOLD;
for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) {
if (fi->second > threshold || (vit.count(fi->first) > 0)) {
cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl;
}
}
}
return 0;
}
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