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-rw-r--r--training/fast_align.cc281
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diff --git a/training/fast_align.cc b/training/fast_align.cc
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index 7492d26f..00000000
--- a/training/fast_align.cc
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@@ -1,281 +0,0 @@
-#include <iostream>
-#include <cmath>
-
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "m.h"
-#include "corpus_tools.h"
-#include "stringlib.h"
-#include "filelib.h"
-#include "ttables.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()
- ("input,i",po::value<string>(),"Parallel corpus input file")
- ("reverse,r","Reverse estimation (swap source and target during training)")
- ("iterations,I",po::value<unsigned>()->default_value(5),"Number of iterations of EM training")
- //("bidir,b", "Run bidirectional alignment")
- ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal")
- ("prob_align_null", po::value<double>()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?")
- ("diagonal_tension,T", po::value<double>()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (<1 = flat >1 = sharp)")
- ("variational_bayes,v","Infer VB estimate of parameters under a symmetric Dirichlet prior")
- ("alpha,a", po::value<double>()->default_value(0.01), "Hyperparameter for optional Dirichlet prior")
- ("no_null_word,N","Do not generate from a null token")
- ("output_parameters,p", "Write model parameters instead of alignments")
- ("beam_threshold,t",po::value<double>()->default_value(-4),"When writing parameters, log_10 of beam threshold for writing parameter (-10000 to include everything, 0 max parameter only)")
- ("hide_training_alignments,H", "Hide training alignments (only useful if you want to use -x option and just compute testset statistics)")
- ("testset,x", po::value<string>(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model")
- ("no_add_viterbi,V","When writing model parameters, 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 (conf->count("help") || conf->count("input") == 0) {
- cerr << "Usage " << argv[0] << " [OPTIONS] -i 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 = conf["input"].as<string>();
- const bool reverse = conf.count("reverse") > 0;
- 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 bool write_alignments = (conf.count("output_parameters") == 0);
- const double diagonal_tension = conf["diagonal_tension"].as<double>();
- const double prob_align_null = conf["prob_align_null"].as<double>();
- const bool hide_training_alignments = (conf.count("hide_training_alignments") > 0);
- string testset;
- if (conf.count("testset")) testset = conf["testset"].as<string>();
- const double prob_align_not_null = 1.0 - prob_align_null;
- const double alpha = conf["alpha"].as<double>();
- const bool favor_diagonal = conf.count("favor_diagonal");
- if (variational_bayes && alpha <= 0.0) {
- cerr << "--alpha must be > 0\n";
- return 1;
- }
-
- TTable s2t, t2s;
- TTable::Word2Word2Double s2t_viterbi;
- double tot_len_ratio = 0;
- double mean_srclen_multiplier = 0;
- vector<double> unnormed_a_i;
- 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;
- string ssrc, strg;
- vector<WordID> src, trg;
- 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; }
- src.clear(); trg.clear();
- CorpusTools::ReadLine(line, &src, &trg);
- if (reverse) swap(src, trg);
- if (src.size() == 0 || trg.size() == 0) {
- cerr << "Error: " << lc << "\n" << line << endl;
- return 1;
- }
- 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];
- double sum = 0;
- const double j_over_ts = double(j) / trg.size();
- double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1)
- if (use_null) {
- if (favor_diagonal) prob_a_i = prob_align_null;
- probs[0] = s2t.prob(kNULL, f_j) * prob_a_i;
- sum += probs[0];
- }
- double az = 0;
- if (favor_diagonal) {
- 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];
- }
- az /= prob_align_not_null;
- }
- for (int i = 1; i <= src.size(); ++i) {
- if (favor_diagonal)
- prob_a_i = unnormed_a_i[i-1] / az;
- probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i;
- sum += probs[i];
- }
- if (final_iteration) {
- if (add_viterbi || write_alignments) {
- WordID max_i = 0;
- double max_p = -1;
- int max_index = -1;
- if (use_null) {
- max_i = kNULL;
- max_index = 0;
- max_p = probs[0];
- }
- for (int i = 1; i <= src.size(); ++i) {
- if (probs[i] > max_p) {
- max_index = i;
- max_p = probs[i];
- max_i = src[i-1];
- }
- }
- if (!hide_training_alignments && write_alignments) {
- if (max_index > 0) {
- if (first_al) first_al = false; else cout << ' ';
- if (reverse)
- cout << j << '-' << (max_index - 1);
- else
- cout << (max_index - 1) << '-' << j;
- }
- }
- s2t_viterbi[max_i][f_j] = 1.0;
- }
- } else {
- if (use_null)
- s2t.Increment(kNULL, f_j, probs[0] / sum);
- for (int i = 1; i <= src.size(); ++i)
- s2t.Increment(src[i-1], f_j, probs[i] / sum);
- }
- likelihood += log(sum);
- }
- if (write_alignments && final_iteration && !hide_training_alignments) cout << endl;
- }
-
- // 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;
- if (!final_iteration) {
- if (variational_bayes)
- s2t.NormalizeVB(alpha);
- else
- s2t.Normalize();
- }
- }
- if (testset.size()) {
- ReadFile rf(testset);
- istream& in = *rf.stream();
- int lc = 0;
- double tlp = 0;
- string line;
- while (getline(in, line)) {
- ++lc;
- vector<WordID> src, trg;
- CorpusTools::ReadLine(line, &src, &trg);
- cout << TD::GetString(src) << " ||| " << TD::GetString(trg) << " |||";
- if (reverse) swap(src, trg);
- double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier);
- if (src.size() > unnormed_a_i.size())
- unnormed_a_i.resize(src.size());
-
- // compute likelihood
- for (int j = 0; j < trg.size(); ++j) {
- const WordID& f_j = trg[j];
- double sum = 0;
- int a_j = 0;
- double max_pat = 0;
- const double j_over_ts = double(j) / trg.size();
- double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1)
- if (use_null) {
- if (favor_diagonal) prob_a_i = prob_align_null;
- max_pat = s2t.prob(kNULL, f_j) * prob_a_i;
- sum += max_pat;
- }
- double az = 0;
- if (favor_diagonal) {
- 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];
- }
- az /= prob_align_not_null;
- }
- for (int i = 1; i <= src.size(); ++i) {
- if (favor_diagonal)
- prob_a_i = unnormed_a_i[i-1] / az;
- double pat = s2t.prob(src[i-1], f_j) * prob_a_i;
- if (pat > max_pat) { max_pat = pat; a_j = i; }
- sum += pat;
- }
- log_prob += log(sum);
- if (write_alignments) {
- if (a_j > 0) {
- cout << ' ';
- if (reverse)
- cout << j << '-' << (a_j - 1);
- else
- cout << (a_j - 1) << '-' << j;
- }
- }
- }
- tlp += log_prob;
- cout << " ||| " << log_prob << endl << flush;
- } // loop over test set sentences
- cerr << "TOTAL LOG PROB " << tlp << endl;
- }
-
- if (write_alignments) return 0;
-
- for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) {
- const TTable::Word2Double& cpd = ei->second;
- const TTable::Word2Double& vit = s2t_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.find(fi->first) != vit.end())) {
- cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl;
- }
- }
- }
- return 0;
-}
-
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