diff options
Diffstat (limited to 'mira')
| -rw-r--r-- | mira/Makefile.am | 6 | ||||
| -rw-r--r-- | mira/kbest_mira.cc | 309 |
2 files changed, 0 insertions, 315 deletions
diff --git a/mira/Makefile.am b/mira/Makefile.am deleted file mode 100644 index 3f8f17cd..00000000 --- a/mira/Makefile.am +++ /dev/null @@ -1,6 +0,0 @@ -bin_PROGRAMS = kbest_mira - -kbest_mira_SOURCES = kbest_mira.cc -kbest_mira_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/klm/search/libksearch.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz - -AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc deleted file mode 100644 index 8b7993dd..00000000 --- a/mira/kbest_mira.cc +++ /dev/null @@ -1,309 +0,0 @@ -#include <sstream> -#include <iostream> -#include <vector> -#include <cassert> -#include <cmath> -#include <tr1/memory> - -#include <boost/program_options.hpp> -#include <boost/program_options/variables_map.hpp> - -#include "hg_sampler.h" -#include "sentence_metadata.h" -#include "scorer.h" -#include "verbose.h" -#include "viterbi.h" -#include "hg.h" -#include "prob.h" -#include "kbest.h" -#include "ff_register.h" -#include "decoder.h" -#include "filelib.h" -#include "fdict.h" -#include "weights.h" -#include "sparse_vector.h" -#include "sampler.h" - -using namespace std; -namespace po = boost::program_options; - -bool invert_score; -std::tr1::shared_ptr<MT19937> rng; - -void RandomPermutation(int len, vector<int>* p_ids) { - vector<int>& ids = *p_ids; - ids.resize(len); - for (int i = 0; i < len; ++i) ids[i] = i; - for (int i = len; i > 0; --i) { - int j = rng->next() * i; - if (j == i) i--; - swap(ids[i-1], ids[j]); - } -} - -bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { - po::options_description opts("Configuration options"); - opts.add_options() - ("input_weights,w",po::value<string>(),"Input feature weights file") - ("source,i",po::value<string>(),"Source file for development set") - ("passes,p", po::value<int>()->default_value(15), "Number of passes through the training data") - ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)") - ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)") - ("max_step_size,C", po::value<double>()->default_value(0.01), "regularization strength (C)") - ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by") - ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles") - ("sample_forest,f", "Instead of a k-best list, sample k hypotheses from the decoder's forest") - ("sample_forest_unit_weight_vector,x", "Before sampling (must use -f option), rescale the weight vector used so it has unit length; this may improve the quality of the samples") - ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)") - ("decoder_config,c",po::value<string>(),"Decoder configuration file"); - 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_weights") || !conf->count("source") || !conf->count("decoder_config") || !conf->count("reference")) { - cerr << dcmdline_options << endl; - return false; - } - return true; -} - -static const double kMINUS_EPSILON = -1e-6; - -struct HypothesisInfo { - SparseVector<double> features; - double mt_metric; -}; - -struct GoodBadOracle { - std::tr1::shared_ptr<HypothesisInfo> good; - std::tr1::shared_ptr<HypothesisInfo> bad; -}; - -struct TrainingObserver : public DecoderObserver { - TrainingObserver(const int k, const DocScorer& d, bool sf, vector<GoodBadOracle>* o) : ds(d), oracles(*o), kbest_size(k), sample_forest(sf) {} - const DocScorer& ds; - vector<GoodBadOracle>& oracles; - std::tr1::shared_ptr<HypothesisInfo> cur_best; - const int kbest_size; - const bool sample_forest; - - const HypothesisInfo& GetCurrentBestHypothesis() const { - return *cur_best; - } - - virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) { - UpdateOracles(smeta.GetSentenceID(), *hg); - } - - std::tr1::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) { - std::tr1::shared_ptr<HypothesisInfo> h(new HypothesisInfo); - h->features = feats; - h->mt_metric = score; - return h; - } - - void UpdateOracles(int sent_id, const Hypergraph& forest) { - std::tr1::shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good; - std::tr1::shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad; - cur_bad.reset(); // TODO get rid of?? - - if (sample_forest) { - vector<WordID> cur_prediction; - ViterbiESentence(forest, &cur_prediction); - float sentscore = ds[sent_id]->ScoreCandidate(cur_prediction)->ComputeScore(); - cur_best = MakeHypothesisInfo(ViterbiFeatures(forest), sentscore); - - vector<HypergraphSampler::Hypothesis> samples; - HypergraphSampler::sample_hypotheses(forest, kbest_size, &*rng, &samples); - for (unsigned i = 0; i < samples.size(); ++i) { - sentscore = ds[sent_id]->ScoreCandidate(samples[i].words)->ComputeScore(); - if (invert_score) sentscore *= -1.0; - if (!cur_good || sentscore > cur_good->mt_metric) - cur_good = MakeHypothesisInfo(samples[i].fmap, sentscore); - if (!cur_bad || sentscore < cur_bad->mt_metric) - cur_bad = MakeHypothesisInfo(samples[i].fmap, sentscore); - } - } else { - KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size); - for (int i = 0; i < kbest_size; ++i) { - const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d = - kbest.LazyKthBest(forest.nodes_.size() - 1, i); - if (!d) break; - float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore(); - if (invert_score) sentscore *= -1.0; - // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl; - if (i == 0) - cur_best = MakeHypothesisInfo(d->feature_values, sentscore); - if (!cur_good || sentscore > cur_good->mt_metric) - cur_good = MakeHypothesisInfo(d->feature_values, sentscore); - if (!cur_bad || sentscore < cur_bad->mt_metric) - cur_bad = MakeHypothesisInfo(d->feature_values, sentscore); - } - //cerr << "GOOD: " << cur_good->mt_metric << endl; - //cerr << " CUR: " << cur_best->mt_metric << endl; - //cerr << " BAD: " << cur_bad->mt_metric << endl; - } - } -}; - -void ReadTrainingCorpus(const string& fname, vector<string>* c) { - ReadFile rf(fname); - istream& in = *rf.stream(); - string line; - while(in) { - getline(in, line); - if (!in) break; - c->push_back(line); - } -} - -bool ApproxEqual(double a, double b) { - if (a == b) return true; - return (fabs(a-b)/fabs(b)) < 0.000001; -} - -int main(int argc, char** argv) { - register_feature_functions(); - SetSilent(true); // turn off verbose decoder output - - po::variables_map conf; - if (!InitCommandLine(argc, argv, &conf)) return 1; - - if (conf.count("random_seed")) - rng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); - else - rng.reset(new MT19937); - const bool sample_forest = conf.count("sample_forest") > 0; - const bool sample_forest_unit_weight_vector = conf.count("sample_forest_unit_weight_vector") > 0; - if (sample_forest_unit_weight_vector && !sample_forest) { - cerr << "Cannot --sample_forest_unit_weight_vector without --sample_forest" << endl; - return 1; - } - vector<string> corpus; - ReadTrainingCorpus(conf["source"].as<string>(), &corpus); - const string metric_name = conf["mt_metric"].as<string>(); - ScoreType type = ScoreTypeFromString(metric_name); - if (type == TER) { - invert_score = true; - } else { - invert_score = false; - } - DocScorer ds(type, conf["reference"].as<vector<string> >(), ""); - cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl; - if (ds.size() != corpus.size()) { - cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n"; - return 1; - } - - ReadFile ini_rf(conf["decoder_config"].as<string>()); - Decoder decoder(ini_rf.stream()); - - // load initial weights - vector<weight_t>& dense_weights = decoder.CurrentWeightVector(); - SparseVector<weight_t> lambdas; - Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights); - Weights::InitSparseVector(dense_weights, &lambdas); - - const double max_step_size = conf["max_step_size"].as<double>(); - const double mt_metric_scale = conf["mt_metric_scale"].as<double>(); - - assert(corpus.size() > 0); - vector<GoodBadOracle> oracles(corpus.size()); - - TrainingObserver observer(conf["k_best_size"].as<int>(), ds, sample_forest, &oracles); - int cur_sent = 0; - int lcount = 0; - int normalizer = 0; - double tot_loss = 0; - int dots = 0; - int cur_pass = 0; - SparseVector<double> tot; - tot += lambdas; // initial weights - normalizer++; // count for initial weights - int max_iteration = conf["passes"].as<int>() * corpus.size(); - string msg = "# MIRA tuned weights"; - string msga = "# MIRA tuned weights AVERAGED"; - vector<int> order; - RandomPermutation(corpus.size(), &order); - while (lcount <= max_iteration) { - lambdas.init_vector(&dense_weights); - if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; } - if (corpus.size() == cur_sent) { - cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n"; - Weights::ShowLargestFeatures(dense_weights); - cur_sent = 0; - tot_loss = 0; - dots = 0; - ostringstream os; - os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz"; - SparseVector<double> x = tot; - x /= normalizer; - ostringstream sa; - sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz"; - x.init_vector(&dense_weights); - Weights::WriteToFile(os.str(), dense_weights, true, &msg); - ++cur_pass; - RandomPermutation(corpus.size(), &order); - } - if (cur_sent == 0) { - cerr << "PASS " << (lcount / corpus.size() + 1) << endl; - } - decoder.SetId(order[cur_sent]); - double sc = 1.0; - if (sample_forest_unit_weight_vector) { - sc = lambdas.l2norm(); - if (sc > 0) { - for (unsigned i = 0; i < dense_weights.size(); ++i) - dense_weights[i] /= sc; - } - } - decoder.Decode(corpus[order[cur_sent]], &observer); // update oracles - if (sc && sc != 1.0) { - for (unsigned i = 0; i < dense_weights.size(); ++i) - dense_weights[i] *= sc; - } - const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis(); - const HypothesisInfo& cur_good = *oracles[order[cur_sent]].good; - const HypothesisInfo& cur_bad = *oracles[order[cur_sent]].bad; - tot_loss += cur_hyp.mt_metric; - if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) { - const double loss = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights) + - mt_metric_scale * (cur_good.mt_metric - cur_bad.mt_metric); - //cerr << "LOSS: " << loss << endl; - if (loss > 0.0) { - SparseVector<double> diff = cur_good.features; - diff -= cur_bad.features; - double step_size = loss / diff.l2norm_sq(); - //cerr << loss << " " << step_size << " " << diff << endl; - if (step_size > max_step_size) step_size = max_step_size; - lambdas += (cur_good.features * step_size); - lambdas -= (cur_bad.features * step_size); - //cerr << "L: " << lambdas << endl; - } - } - tot += lambdas; - ++normalizer; - ++lcount; - ++cur_sent; - } - cerr << endl; - Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg); - tot /= normalizer; - tot.init_vector(dense_weights); - msg = "# MIRA tuned weights (averaged vector)"; - Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg); - cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n"; - return 0; -} - |