diff options
Diffstat (limited to 'pro-train')
| -rwxr-xr-x | pro-train/dist-pro.pl | 4 | ||||
| -rw-r--r-- | pro-train/mr_pro_map.cc | 37 |
2 files changed, 26 insertions, 15 deletions
diff --git a/pro-train/dist-pro.pl b/pro-train/dist-pro.pl index ba9cdc06..31258fa6 100755 --- a/pro-train/dist-pro.pl +++ b/pro-train/dist-pro.pl @@ -12,7 +12,7 @@ use POSIX ":sys_wait_h"; my $QSUB_CMD = qsub_args(mert_memory()); my $default_jobs = env_default_jobs(); -my $VEST_DIR="$SCRIPT_DIR/../vest"; +my $VEST_DIR="$SCRIPT_DIR/../dpmert"; require "$VEST_DIR/libcall.pl"; # Default settings @@ -338,7 +338,7 @@ while (1){ $mapoutput =~ s/mapinput/mapoutput/; push @mapoutputs, "$dir/splag.$im1/$mapoutput"; $o2i{"$dir/splag.$im1/$mapoutput"} = "$dir/splag.$im1/$shard"; - my $script = "$MAPPER -s $srcFile -l $metric $refs_comma_sep -w $inweights -K $dir/kbest < $dir/splag.$im1/$shard > $dir/splag.$im1/$mapoutput"; + my $script = "$MAPPER -s $srcFile -m $metric $refs_comma_sep -w $inweights -K $dir/kbest < $dir/splag.$im1/$shard > $dir/splag.$im1/$mapoutput"; if ($use_make) { my $script_file = "$dir/scripts/map.$shard"; open F, ">$script_file" or die "Can't write $script_file: $!"; diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc index 0a9b75d7..52b67f32 100644 --- a/pro-train/mr_pro_map.cc +++ b/pro-train/mr_pro_map.cc @@ -13,11 +13,12 @@ #include "filelib.h" #include "stringlib.h" #include "weights.h" -#include "scorer.h" #include "inside_outside.h" #include "hg_io.h" #include "kbest.h" #include "viterbi.h" +#include "ns.h" +#include "ns_docscorer.h" // This is Figure 4 (Algorithm Sampler) from Hopkins&May (2011) @@ -80,7 +81,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { ("kbest_repository,K",po::value<string>()->default_value("./kbest"),"K-best list repository (directory)") ("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)") ("source,s",po::value<string>()->default_value(""), "Source file (ignored, except for AER)") - ("loss_function,l",po::value<string>()->default_value("ibm_bleu"), "Loss function being optimized") + ("evaluation_metric,m",po::value<string>()->default_value("IBM_BLEU"), "Evaluation metric (ibm_bleu, koehn_bleu, nist_bleu, ter, meteor, etc.)") ("kbest_size,k",po::value<unsigned>()->default_value(1500u), "Top k-hypotheses to extract") ("candidate_pairs,G", po::value<unsigned>()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)") ("best_pairs,X", po::value<unsigned>()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)") @@ -109,9 +110,12 @@ struct HypInfo { HypInfo(const vector<WordID>& h, const SparseVector<weight_t>& feats) : hyp(h), g_(-100.0f), x(feats) {} // lazy evaluation - double g(const SentenceScorer& scorer) const { - if (g_ == -100.0f) - g_ = scorer.ScoreCandidate(hyp)->ComputeScore(); + double g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const { + if (g_ == -100.0f) { + SufficientStats ss; + scorer.Evaluate(hyp, &ss); + g_ = metric->ComputeScore(ss); + } return g_; } vector<WordID> hyp; @@ -233,15 +237,21 @@ struct DiffOrder { } }; -void Sample(const unsigned gamma, const unsigned xi, const vector<HypInfo>& J_i, const SentenceScorer& scorer, const bool invert_score, vector<TrainingInstance>* pv) { +void Sample(const unsigned gamma, + const unsigned xi, + const vector<HypInfo>& J_i, + const SegmentEvaluator& scorer, + const EvaluationMetric* metric, + vector<TrainingInstance>* pv) { + const bool invert_score = metric->IsErrorMetric(); vector<TrainingInstance> v1, v2; float avg_diff = 0; for (unsigned 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; - float ga = J_i[a].g(scorer); - float gb = J_i[b].g(scorer); + float ga = J_i[a].g(scorer, metric); + float gb = J_i[b].g(scorer, metric); bool positive = gb < ga; if (invert_score) positive = !positive; const float gdiff = fabs(ga - gb); @@ -288,11 +298,12 @@ int main(int argc, char** argv) { rng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); else rng.reset(new MT19937); - const string loss_function = conf["loss_function"].as<string>(); + const string evaluation_metric = conf["evaluation_metric"].as<string>(); + + EvaluationMetric* metric = EvaluationMetric::Instance(evaluation_metric); + DocumentScorer ds(metric, conf["reference"].as<vector<string> >()); + cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl; - ScoreType type = ScoreTypeFromString(loss_function); - DocScorer ds(type, conf["reference"].as<vector<string> >(), conf["source"].as<string>()); - cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function << endl; Hypergraph hg; string last_file; ReadFile in_read(conf["input"].as<string>()); @@ -335,7 +346,7 @@ int main(int argc, char** argv) { Dedup(&J_i); WriteKBest(kbest_file, J_i); - Sample(gamma, xi, J_i, *ds[sent_id], (type == TER), &v); + Sample(gamma, xi, J_i, *ds[sent_id], metric, &v); for (unsigned i = 0; i < v.size(); ++i) { const TrainingInstance& vi = v[i]; cout << vi.y << "\t" << vi.x << endl; |