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authorChris Dyer <cdyer@cs.cmu.edu>2011-05-03 02:02:29 -0400
committerChris Dyer <cdyer@cs.cmu.edu>2011-05-03 02:02:29 -0400
commit7892b332a583d235fd339de86df8ee6f5d8209f4 (patch)
tree7ada3d606993a7666367971d53bebbcf6b79f265
parent520e03c12ffb6da89a66ecf9b9e46eeb3a3c8087 (diff)
add mira
-rw-r--r--mira/Makefile.am6
-rw-r--r--mira/kbest_mira.cc237
2 files changed, 243 insertions, 0 deletions
diff --git a/mira/Makefile.am b/mira/Makefile.am
new file mode 100644
index 00000000..7b4a4e12
--- /dev/null
+++ b/mira/Makefile.am
@@ -0,0 +1,6 @@
+bin_PROGRAMS = kbest_mira
+
+kbest_mira_SOURCES = kbest_mira.cc
+kbest_mira_LDADD = $(top_srcdir)/decoder/libcdec.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
new file mode 100644
index 00000000..7ff207a8
--- /dev/null
+++ b/mira/kbest_mira.cc
@@ -0,0 +1,237 @@
+#include <sstream>
+#include <iostream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include "config.h"
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#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"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+void SanityCheck(const vector<double>& w) {
+ for (int i = 0; i < w.size(); ++i) {
+ assert(!isnan(w[i]));
+ assert(!isinf(w[i]));
+ }
+}
+
+struct FComp {
+ const vector<double>& w_;
+ FComp(const vector<double>& w) : w_(w) {}
+ bool operator()(int a, int b) const {
+ return fabs(w_[a]) > fabs(w_[b]);
+ }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+ vector<int> fnums(w.size());
+ for (int i = 0; i < w.size(); ++i)
+ fnums[i] = i;
+ vector<int>::iterator mid = fnums.begin();
+ mid += (w.size() > 10 ? 10 : w.size());
+ partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+ cerr << "TOP FEATURES:";
+ for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+ cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+ }
+ cerr << endl;
+}
+
+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")
+ ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)")
+ ("mt_metric,m",po::value<string>()->default_value("ter"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)")
+ ("max_step_size,C", po::value<double>()->default_value(0.0001), "maximum step size (C)")
+ ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by")
+ ("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 {
+ shared_ptr<HypothesisInfo> good;
+ shared_ptr<HypothesisInfo> bad;
+};
+
+struct TrainingObserver : public DecoderObserver {
+ TrainingObserver(const DocScorer& d, vector<GoodBadOracle>* o) : ds(d), oracles(*o) {}
+ const DocScorer& ds;
+ vector<GoodBadOracle>& oracles;
+ shared_ptr<HypothesisInfo> cur_best;
+
+ const HypothesisInfo& GetCurrentBestHypothesis() const {
+ return *cur_best;
+ }
+
+ virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
+ UpdateOracles(smeta.GetSentenceID(), *hg);
+ }
+
+ shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) {
+ shared_ptr<HypothesisInfo> h(new HypothesisInfo);
+ h->features = feats;
+ h->mt_metric = score;
+ return h;
+ }
+
+ void UpdateOracles(int sent_id, const Hypergraph& forest) {
+ int kbest_size = 330;
+ shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good;
+ shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad;
+ cur_bad.reset(); // TODO get rid of??
+ 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();
+// 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 << " BAD: " << cur_bad->mt_metric << endl;
+ cerr << " #1: " << cur_best->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;
+
+ vector<string> corpus;
+ ReadTrainingCorpus(conf["source"].as<string>(), &corpus);
+ const string metric_name = conf["mt_metric"].as<string>();
+ ScoreType type = ScoreTypeFromString(metric_name);
+ 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;
+ }
+ // load initial weights
+ Weights weights;
+ weights.InitFromFile(conf["input_weights"].as<string>());
+ SparseVector<double> lambdas;
+ weights.InitSparseVector(&lambdas);
+
+ // freeze feature set (should be optional?)
+ const bool freeze_feature_set = true;
+ if (freeze_feature_set) FD::Freeze();
+
+ ReadFile ini_rf(conf["decoder_config"].as<string>());
+ Decoder decoder(ini_rf.stream());
+ 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(ds, &oracles);
+ int cur_sent = 0;
+ bool converged = false;
+ vector<double> dense_weights;
+ while (!converged) {
+ dense_weights.clear();
+ weights.InitFromVector(lambdas);
+ weights.InitVector(&dense_weights);
+ decoder.SetWeights(dense_weights);
+ if (corpus.size() == cur_sent) cur_sent = 0;
+ decoder.SetId(cur_sent);
+ decoder.Decode(corpus[cur_sent], &observer); // update oracles
+ const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis();
+ const HypothesisInfo& cur_good = *oracles[cur_sent].good;
+ const HypothesisInfo& cur_bad = *oracles[cur_sent].bad;
+ 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;
+ }
+ }
+ ++cur_sent;
+ static int cc = 0; ++cc; if (cc==250) converged = true;
+ }
+ weights.WriteToFile("-");
+ return 0;
+}
+