diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/dpmert/lo_test.cc | 2 | ||||
| -rw-r--r-- | training/mira/kbest_cut_mira.cc | 61 | ||||
| -rwxr-xr-x | training/mira/mira.py | 11 | ||||
| -rw-r--r-- | training/pro/mr_pro_map.cc | 26 | ||||
| -rw-r--r-- | training/utils/grammar_convert.cc | 21 |
5 files changed, 71 insertions, 50 deletions
diff --git a/training/dpmert/lo_test.cc b/training/dpmert/lo_test.cc index d89bcd99..b8776169 100644 --- a/training/dpmert/lo_test.cc +++ b/training/dpmert/lo_test.cc @@ -56,7 +56,7 @@ BOOST_AUTO_TEST_CASE(TestConvexHull) { } BOOST_AUTO_TEST_CASE(TestConvexHullInside) { - const string json = "{\"rules\":[1,\"[X] ||| a\",2,\"[X] ||| A [1]\",3,\"[X] ||| c\",4,\"[X] ||| C [1]\",5,\"[X] ||| [1] B [2]\",6,\"[X] ||| [1] b [2]\",7,\"[X] ||| X [1]\",8,\"[X] ||| Z [1]\"],\"features\":[\"f1\",\"f2\",\"Feature_1\",\"Feature_0\",\"Model_0\",\"Model_1\",\"Model_2\",\"Model_3\",\"Model_4\",\"Model_5\",\"Model_6\",\"Model_7\"],\"edges\":[{\"tail\":[],\"feats\":[],\"rule\":1}],\"node\":{\"in_edges\":[0]},\"edges\":[{\"tail\":[0],\"feats\":[0,-0.8,1,-0.1],\"rule\":2}],\"node\":{\"in_edges\":[1]},\"edges\":[{\"tail\":[],\"feats\":[1,-1],\"rule\":3}],\"node\":{\"in_edges\":[2]},\"edges\":[{\"tail\":[2],\"feats\":[0,-0.2,1,-0.1],\"rule\":4}],\"node\":{\"in_edges\":[3]},\"edges\":[{\"tail\":[1,3],\"feats\":[0,-1.2,1,-0.2],\"rule\":5},{\"tail\":[1,3],\"feats\":[0,-0.5,1,-1.3],\"rule\":6}],\"node\":{\"in_edges\":[4,5]},\"edges\":[{\"tail\":[4],\"feats\":[0,-0.5,1,-0.8],\"rule\":7},{\"tail\":[4],\"feats\":[0,-0.7,1,-0.9],\"rule\":8}],\"node\":{\"in_edges\":[6,7]}}"; + const string json = "{\"rules\":[1,\"[X] ||| a ||| a\",2,\"[X] ||| A [X] ||| A [1]\",3,\"[X] ||| c ||| c\",4,\"[X] ||| C [X] ||| C [1]\",5,\"[X] ||| [X] B [X] ||| [1] B [2]\",6,\"[X] ||| [X] b [X] ||| [1] b [2]\",7,\"[X] ||| X [X] ||| X [1]\",8,\"[X] ||| Z [X] ||| Z [1]\"],\"features\":[\"f1\",\"f2\",\"Feature_1\",\"Feature_0\",\"Model_0\",\"Model_1\",\"Model_2\",\"Model_3\",\"Model_4\",\"Model_5\",\"Model_6\",\"Model_7\"],\"edges\":[{\"tail\":[],\"feats\":[],\"rule\":1}],\"node\":{\"in_edges\":[0]},\"edges\":[{\"tail\":[0],\"feats\":[0,-0.8,1,-0.1],\"rule\":2}],\"node\":{\"in_edges\":[1]},\"edges\":[{\"tail\":[],\"feats\":[1,-1],\"rule\":3}],\"node\":{\"in_edges\":[2]},\"edges\":[{\"tail\":[2],\"feats\":[0,-0.2,1,-0.1],\"rule\":4}],\"node\":{\"in_edges\":[3]},\"edges\":[{\"tail\":[1,3],\"feats\":[0,-1.2,1,-0.2],\"rule\":5},{\"tail\":[1,3],\"feats\":[0,-0.5,1,-1.3],\"rule\":6}],\"node\":{\"in_edges\":[4,5]},\"edges\":[{\"tail\":[4],\"feats\":[0,-0.5,1,-0.8],\"rule\":7},{\"tail\":[4],\"feats\":[0,-0.7,1,-0.9],\"rule\":8}],\"node\":{\"in_edges\":[6,7]}}"; Hypergraph hg; istringstream instr(json); HypergraphIO::ReadFromJSON(&instr, &hg); diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc index cde65332..724b1853 100644 --- a/training/mira/kbest_cut_mira.cc +++ b/training/mira/kbest_cut_mira.cc @@ -341,23 +341,22 @@ struct BasicObserver: public DecoderObserver { }; struct TrainingObserver : public DecoderObserver { - TrainingObserver(const int k, const DocScorer& d, vector<GoodBadOracle>* o, vector<ScoreP>* cbs) : ds(d), oracles(*o), corpus_bleu_sent_stats(*cbs), kbest_size(k) { - - - if(!pseudo_doc && !sent_approx) - if(cur_pass > 0) //calculate corpus bleu score from previous iterations 1-best for BLEU gain - { - ScoreP acc; - for (int ii = 0; ii < corpus_bleu_sent_stats.size(); ii++) { - if (!acc) { acc = corpus_bleu_sent_stats[ii]->GetZero(); } - acc->PlusEquals(*corpus_bleu_sent_stats[ii]); - - } - corpus_bleu_stats = acc; - corpus_bleu_score = acc->ComputeScore(); + TrainingObserver(const int k, + const DocScorer& d, + vector<GoodBadOracle>* o, + vector<ScoreP>* cbs) : ds(d), oracles(*o), corpus_bleu_sent_stats(*cbs), kbest_size(k) { + if(!pseudo_doc && !sent_approx) { + if(cur_pass > 0) { //calculate corpus bleu score from previous iterations 1-best for BLEU gain + ScoreP acc; + for (int ii = 0; ii < corpus_bleu_sent_stats.size(); ii++) { + if (!acc) { acc = corpus_bleu_sent_stats[ii]->GetZero(); } + acc->PlusEquals(*corpus_bleu_sent_stats[ii]); + } + corpus_bleu_stats = acc; + corpus_bleu_score = acc->ComputeScore(); } - -} + } + } const DocScorer& ds; vector<ScoreP>& corpus_bleu_sent_stats; vector<GoodBadOracle>& oracles; @@ -461,7 +460,6 @@ struct TrainingObserver : public DecoderObserver { } else //use sentence-level smoothing ( used when cur_pass=0 if not pseudo_doc) { - sentscore = mt_metric_scale * (ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore()); } @@ -575,19 +573,15 @@ void ReadTrainingCorpus(const string& fname, vector<string>* c) { } } -void ReadPastTranslationForScore(const int cur_pass, vector<ScoreP>* c, DocScorer& ds, const string& od) -{ - cerr << "Reading BLEU gain file "; +void ReadPastTranslationForScore(const int cur_pass, vector<ScoreP>* c, DocScorer& ds, const string& od) { + cerr << "Reading previous score file "; string fname; - if(cur_pass == 0) - { - fname = od + "/run.raw.init"; - } - else - { - int last_pass = cur_pass - 1; - fname = od + "/run.raw." + boost::lexical_cast<std::string>(last_pass) + ".B"; - } + if (cur_pass == 0) { + fname = od + "/run.raw.init"; + } else { + int last_pass = cur_pass - 1; + fname = od + "/run.raw." + boost::lexical_cast<std::string>(last_pass) + ".B"; + } cerr << fname << "\n"; ReadFile rf(fname); istream& in = *rf.stream(); @@ -604,7 +598,6 @@ void ReadPastTranslationForScore(const int cur_pass, vector<ScoreP>* c, DocScore if (!acc) { acc = sentscore->GetZero(); } acc->PlusEquals(*sentscore); ++lc; - } assert(lc > 0); @@ -612,7 +605,6 @@ void ReadPastTranslationForScore(const int cur_pass, vector<ScoreP>* c, DocScore string details; acc->ScoreDetails(&details); cerr << "Previous run: " << details << score << endl; - } @@ -672,10 +664,9 @@ int main(int argc, char** argv) { //check training pass,if >0, then use previous iterations corpus bleu stats cur_pass = stream ? 0 : conf["pass"].as<int>(); - if(cur_pass > 0) - { - ReadPastTranslationForScore(cur_pass, &corpus_bleu_sent_stats, *ds, output_dir); - } + if(cur_pass > 0) { + ReadPastTranslationForScore(cur_pass, &corpus_bleu_sent_stats, *ds, output_dir); + } cerr << "Using optimizer:" << optimizer << endl; diff --git a/training/mira/mira.py b/training/mira/mira.py index 539a0b0e..691a62a6 100755 --- a/training/mira/mira.py +++ b/training/mira/mira.py @@ -203,14 +203,15 @@ def main(): if have_mpl: graph_file = graph(args.output_dir, hope_best_fear, args.metric) dev_results, dev_bleu = evaluate(args.devset, args.weights, args.config, - script_dir, args.output_dir) + script_dir, args.output_dir, args.jobs) if args.test: if args.test_config: test_results, test_bleu = evaluate(args.test, args.weights, - args.test_config, script_dir, args.output_dir) + args.test_config, script_dir, args.output_dir, + args.jobs) else: test_results, test_bleu = evaluate(args.test, args.weights, args.config, - script_dir, args.output_dir) + script_dir, args.output_dir, args.jobs) else: test_results = '' test_bleu = '' @@ -240,11 +241,11 @@ def graph(output_dir, hope_best_fear, metric): return graph_file #evaluate a given test set using decode-and-evaluate.pl -def evaluate(testset, weights, ini, script_dir, out_dir): +def evaluate(testset, weights, ini, script_dir, out_dir, jobs): evaluator = '{}/../utils/decode-and-evaluate.pl'.format(script_dir) try: p = subprocess.Popen([evaluator, '-c', ini, '-w', weights, '-i', testset, - '-d', out_dir, '--jobs', args.jobs], stdout=subprocess.PIPE) + '-d', out_dir, '--jobs', str(jobs)], stdout=subprocess.PIPE) results, err = p.communicate() bleu, results = results.split('\n',1) except subprocess.CalledProcessError: diff --git a/training/pro/mr_pro_map.cc b/training/pro/mr_pro_map.cc index a5e6e48f..da58cd24 100644 --- a/training/pro/mr_pro_map.cc +++ b/training/pro/mr_pro_map.cc @@ -88,23 +88,43 @@ struct DiffOrder { } }; -void Sample(const unsigned gamma, +double LengthDifferenceStdDev(const training::CandidateSet& J_i, int n) { + double sum = 0; + for (int i = 0; i < n; ++i) { + const size_t a = rng->inclusive(0, J_i.size() - 1)(); + const size_t b = rng->inclusive(0, J_i.size() - 1)(); + if (a == b) { --i; continue; } + double p = J_i[a].ewords.size(); + p -= J_i[b].ewords.size(); + sum += p * p; // mean is 0 by construction + } + return max(sqrt(sum / n), 2.0); +}; + +void Sample(const int gamma, const unsigned xi, const training::CandidateSet& J_i, const EvaluationMetric* metric, vector<TrainingInstance>* pv) { + const double len_stddev = LengthDifferenceStdDev(J_i, 5000); const bool invert_score = metric->IsErrorMetric(); vector<TrainingInstance> v1, v2; float avg_diff = 0; - for (unsigned i = 0; i < gamma; ++i) { + const double z_score_threshold=2; + for (int i = 0; i < gamma; ++i) { const size_t a = rng->inclusive(0, J_i.size() - 1)(); const size_t b = rng->inclusive(0, J_i.size() - 1)(); - if (a == b) continue; + if (a == b) { --i; continue; } + double z_score = fabs(((int)J_i[a].ewords.size() - (int)J_i[b].ewords.size()) / len_stddev); + // variation on Nakov et al. (2011) + if (z_score > z_score_threshold) { --i; continue; } float ga = metric->ComputeScore(J_i[a].eval_feats); float gb = metric->ComputeScore(J_i[b].eval_feats); bool positive = gb < ga; if (invert_score) positive = !positive; const float gdiff = fabs(ga - gb); + //cerr << ((int)J_i[a].ewords.size() - (int)J_i[b].ewords.size()) << endl; + //cerr << (ga - gb) << endl; if (!gdiff) continue; avg_diff += gdiff; SparseVector<weight_t> xdiff = (J_i[a].fmap - J_i[b].fmap).erase_zeros(); diff --git a/training/utils/grammar_convert.cc b/training/utils/grammar_convert.cc index 607a7cb9..5c1b4d4a 100644 --- a/training/utils/grammar_convert.cc +++ b/training/utils/grammar_convert.cc @@ -56,15 +56,22 @@ int GetOrCreateNode(const WordID& lhs, map<WordID, int>* lhs2node, Hypergraph* h return node_id - 1; } +void AddDummyGoalNode(Hypergraph* hg) { + static const int kGOAL = -TD::Convert("Goal"); + static TRulePtr kGOAL_RULE(new TRule("[Goal] ||| [X] ||| [1]")); + unsigned old_goal_node_idx = hg->nodes_.size() - 1; + HG::Node* goal_node = hg->AddNode(kGOAL); + goal_node->node_hash = goal_node->id_ * 10 + 1; + TailNodeVector tail(1, old_goal_node_idx); + HG::Edge* new_edge = hg->AddEdge(kGOAL_RULE, tail); + hg->ConnectEdgeToHeadNode(new_edge, goal_node); +} + void FilterAndCheckCorrectness(int goal, Hypergraph* hg) { if (goal < 0) { cerr << "Error! [S] not found in grammar!\n"; exit(1); } - if (hg->nodes_[goal].in_edges_.size() != 1) { - cerr << "Error! [S] has more than one rewrite!\n"; - exit(1); - } int old_size = hg->nodes_.size(); hg->TopologicallySortNodesAndEdges(goal); if (hg->nodes_.size() != old_size) { @@ -292,10 +299,10 @@ int main(int argc, char **argv) { int lc = 0; Hypergraph hg; map<WordID, int> lhs2node; + string line; while(*in) { - string line; + getline(*in,line); ++lc; - getline(*in, line); if (is_json_input) { if (line.empty() || line[0] == '#') continue; string ref; @@ -319,6 +326,7 @@ int main(int argc, char **argv) { if (line.empty()) { int goal = lhs2node[kSTART] - 1; FilterAndCheckCorrectness(goal, &hg); + AddDummyGoalNode(&hg); ProcessHypergraph(w, conf, "", &hg); hg.clear(); lhs2node.clear(); @@ -342,6 +350,7 @@ int main(int argc, char **argv) { edge->feature_values_ = tr->scores_; Hypergraph::Node* node = &hg.nodes_[head]; hg.ConnectEdgeToHeadNode(edge, node); + node->node_hash = lc; } } } |