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Diffstat (limited to 'training/fast_align.cc')
| -rw-r--r-- | training/fast_align.cc | 271 |
1 files changed, 271 insertions, 0 deletions
diff --git a/training/fast_align.cc b/training/fast_align.cc new file mode 100644 index 00000000..0d7b0202 --- /dev/null +++ b/training/fast_align.cc @@ -0,0 +1,271 @@ +#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)") + ("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; +} + +double PosteriorInference(const vector<WordID>& src, const vector<WordID>& trg) { + double llh = 0; + static vector<double> unnormed_a_i; + if (src.size() > unnormed_a_i.size()) + unnormed_a_i.resize(src.size()); + return llh; +} + +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>(); + 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 (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) 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 ssrc, strg, line; + while (getline(in, line)) { + ++lc; + vector<WordID> src, trg; + CorpusTools::ReadLine(line, &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; + 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; + sum += s2t.prob(kNULL, f_j) * prob_a_i; + } + 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; + sum += s2t.prob(src[i-1], f_j) * prob_a_i; + } + log_prob += log(sum); + } + tlp += log_prob; + cerr << ssrc << " ||| " << strg << " ||| " << log_prob << endl; + } + 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; +} + |