summaryrefslogtreecommitdiff
diff options
context:
space:
mode:
-rw-r--r--training/Makefile.am4
-rw-r--r--training/lbl_model.cc131
2 files changed, 135 insertions, 0 deletions
diff --git a/training/Makefile.am b/training/Makefile.am
index d2f1ccc5..330341ac 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -1,5 +1,6 @@
bin_PROGRAMS = \
model1 \
+ lbl_model \
test_ngram \
mr_em_map_adapter \
mr_em_adapted_reduce \
@@ -49,6 +50,9 @@ test_ngram_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteva
model1_SOURCES = model1.cc ttables.cc
model1_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
+lbl_model_SOURCES = lbl_model.cc ttables.cc
+lbl_model_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
+
grammar_convert_SOURCES = grammar_convert.cc
grammar_convert_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz
diff --git a/training/lbl_model.cc b/training/lbl_model.cc
new file mode 100644
index 00000000..72d80a56
--- /dev/null
+++ b/training/lbl_model.cc
@@ -0,0 +1,131 @@
+#include <iostream>
+
+#include "config.h"
+#ifndef HAVE_EIGEN
+ int main() { std::cerr << "Please rebuild with --with-eigen PATH\n"; return 1; }
+#else
+
+#include <cmath>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+#include <Eigen/Dense>
+
+#include "m.h"
+#include "lattice.h"
+#include "stringlib.h"
+#include "filelib.h"
+#include "tdict.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 training")
+ ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (0 = uniform, >0 sharpens)")
+ ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model");
+ 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 diagonal_tension = conf["diagonal_tension"].as<double>();
+ string testset;
+ if (conf.count("testset")) testset = conf["testset"].as<string>();
+
+ double tot_len_ratio = 0;
+ double mean_srclen_multiplier = 0;
+ vector<double> unnormed_a_i;
+ for (int iter = 0; iter < ITERATIONS; ++iter) {
+ cerr << "ITERATION " << (iter + 1) << endl;
+ ReadFile rf(fname);
+ istream& in = *rf.stream();
+ double likelihood = 0;
+ double denom = 0.0;
+ int lc = 0;
+ bool flag = false;
+ string line;
+ string ssrc, strg;
+ 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; }
+ 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);
+ }
+ if (src.size() > unnormed_a_i.size())
+ unnormed_a_i.resize(src.size());
+ if (iter == 0)
+ tot_len_ratio += static_cast<double>(trg.size()) / static_cast<double>(src.size());
+ denom += trg.size();
+ vector<double> probs(src.size() + 1);
+ bool first_al = true; // used for write_alignments
+ for (int j = 0; j < trg.size(); ++j) {
+ const WordID& f_j = trg[j][0].label;
+ double sum = 0;
+ const double j_over_ts = double(j) / trg.size();
+ double prob_a_i = 1.0 / src.size();
+ double az = 0;
+ for (int ta = 0; ta < src.size(); ++ta) {
+ unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension);
+ az += unnormed_a_i[ta];
+ }
+ for (int i = 1; i <= src.size(); ++i) {
+ prob_a_i = unnormed_a_i[i-1] / az;
+ probs[i] = 1; // tt.prob(src[i-1][0].label, f_j) * prob_a_i;
+ sum += probs[i];
+ }
+ }
+ }
+
+ // log(e) = 1.0
+ double base2_likelihood = likelihood / log(2);
+
+ if (flag) { cerr << endl; }
+ if (iter == 0) {
+ mean_srclen_multiplier = tot_len_ratio / lc;
+ cerr << "expected target length = source length * " << mean_srclen_multiplier << 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;
+ }
+ return 0;
+}
+
+#endif
+