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-rw-r--r--dtrain/dtrain.cc47
-rw-r--r--dtrain/dtrain.h5
-rw-r--r--dtrain/pairsampling.h41
-rw-r--r--dtrain/score.cc16
-rw-r--r--dtrain/score.h4
5 files changed, 45 insertions, 68 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 3dee10f2..e817e7ab 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -30,7 +30,6 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input")
("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)")
("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
- ("inc_correct", po::value<bool>()->zero_tokens(), "include correctly ranked pairs into updates")
("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent of features after each epoch")
("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU")
#ifdef DTRAIN_LOCAL
@@ -122,9 +121,6 @@ main(int argc, char** argv)
HSReporter rep(task_id);
bool keep = false;
if (cfg.count("keep")) keep = true;
- bool inc_correct = false;
- if (cfg.count("inc_correct"))
- inc_correct = true;
const unsigned k = cfg["k"].as<unsigned>();
const unsigned N = cfg["N"].as<unsigned>();
@@ -226,7 +222,6 @@ main(int argc, char** argv)
score_t max_score = 0.;
unsigned best_it = 0;
float overall_time = 0.;
- unsigned pair_count = 0, feature_count = 0;
// output cfg
if (!quiet) {
@@ -250,8 +245,6 @@ main(int argc, char** argv)
cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl;
if (cfg.count("l1_reg"))
cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl;
- if (inc_correct)
- cerr << setw(25) << "inc. correct " << inc_correct << endl;
if (rescale)
cerr << setw(25) << "rescale " << rescale << endl;
cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl;
@@ -420,36 +413,18 @@ main(int argc, char** argv)
if (pair_sampling == "PRO")
PROsampling(samples, pairs, pair_threshold);
npairs += pairs.size();
- pair_count += 2*pairs.size();
for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin();
it != pairs.end(); it++) {
- score_t rank_error = it->second.score - it->first.score;
- feature_count += it->first.f.size() + it->second.f.size();
- if (!gamma) {
- // perceptron
- if (rank_error > 0) {
- SparseVector<weight_t> diff_vec = it->second.f - it->first.f;
- lambdas.plus_eq_v_times_s(diff_vec, eta);
- rank_errors++;
- } else {
- if (inc_correct) {
- SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
- lambdas.plus_eq_v_times_s(diff_vec, eta);
- }
- }
- if (it->first.model - it->second.model < 1) margin_violations++;
- } else {
- // SVM
- score_t margin = it->first.model - it->second.model;
- if (rank_error > 0 || margin < 1) {
- SparseVector<weight_t> diff_vec = it->second.f - it->first.f;
- lambdas.plus_eq_v_times_s(diff_vec, eta);
- if (rank_error > 0) rank_errors++;
- if (margin < 1) margin_violations++;
- }
- // regularization
- lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
+ bool rank_error = it->first.model <= it->second.model;
+ if (rank_error) rank_errors++;
+ score_t margin = fabs(it->first.model - it->second.model);
+ if (!rank_error && margin < 1) margin_violations++;
+ if (rank_error || (gamma && margin<1)) {
+ SparseVector<weight_t> diff_vec = it->first.f - it->second.f;
+ lambdas.plus_eq_v_times_s(diff_vec, eta);
+ if (gamma)
+ lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs));
}
}
@@ -553,8 +528,6 @@ main(int argc, char** argv)
cerr << " avg # margin viol: ";
cerr << margin_violations/(float)in_sz << endl;
cerr << " non0 feature count: " << nonz << endl;
- cerr << " avg f count: ";
- cerr << feature_count/(float)pair_count << endl;
}
if (hstreaming) {
@@ -580,7 +553,7 @@ main(int argc, char** argv)
overall_time += time_diff;
if (!quiet) {
cerr << _p2 << _np << "(time " << time_diff/60. << " min, ";
- cerr << time_diff/(float)in_sz<< " s/S)" << endl;
+ cerr << time_diff/in_sz << " s/S)" << endl;
}
if (t+1 != T && !quiet) cerr << endl;
diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h
index 7b03d258..15d32e36 100644
--- a/dtrain/dtrain.h
+++ b/dtrain/dtrain.h
@@ -13,7 +13,7 @@
#include "filelib.h"
-//#define DTRAIN_LOCAL
+#define DTRAIN_LOCAL
#define DTRAIN_DOTS 10 // after how many inputs to display a '.'
#define DTRAIN_GRAMMAR_DELIM "########EOS########"
@@ -85,7 +85,8 @@ inline void printWordIDVec(vector<WordID>& v)
}
template<typename T>
-inline T sign(T z) {
+inline T sign(T z)
+{
if (z == 0) return 0;
return z < 0 ? -1 : +1;
}
diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h
index 56702b86..bb01cf4f 100644
--- a/dtrain/pairsampling.h
+++ b/dtrain/pairsampling.h
@@ -15,12 +15,12 @@ accept_pair(score_t a, score_t b, score_t threshold)
inline void
all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused = 1)
{
- for (unsigned i = 0; i < s->size()-1; i++) {
- for (unsigned j = i+1; j < s->size(); j++) {
+ unsigned sz = s->size();
+ for (unsigned i = 0; i < sz-1; i++) {
+ for (unsigned j = i+1; j < sz; j++) {
if (threshold > 0) {
- if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
training.push_back(make_pair((*s)[i], (*s)[j]));
- }
} else {
training.push_back(make_pair((*s)[i], (*s)[j]));
}
@@ -30,14 +30,14 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc
/*
* multipartite ranking
- * sort by bleu
- * compare top 10% to middle 80% and low 10%
- * cmp middle 80% to low 10%
+ * sort (descending) by bleu
+ * compare top X to middle Y and low X
+ * cmp middle Y to low X
*/
bool
_XYX_cmp_hyp_by_score(ScoredHyp a, ScoredHyp b)
{
- return a.score < b.score;
+ return a.score > b.score;
}
inline void
partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float hi_lo)
@@ -47,27 +47,23 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
unsigned sep = round(sz*hi_lo);
for (unsigned i = 0; i < sep; i++) {
for (unsigned j = sep; j < sz; j++) {
- if ((*s)[i].rank < (*s)[j].rank) {
- if (threshold > 0) {
- if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
- training.push_back(make_pair((*s)[i], (*s)[j]));
- }
- } else {
+ if (threshold > 0) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ } else {
+ if((*s)[i].score != (*s)[j].score)
training.push_back(make_pair((*s)[i], (*s)[j]));
- }
}
}
}
for (unsigned i = sep; i < sz-sep; i++) {
for (unsigned j = sz-sep; j < sz; j++) {
- if ((*s)[i].rank < (*s)[j].rank) {
- if (threshold > 0) {
- if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) {
- training.push_back(make_pair((*s)[i], (*s)[j]));
- }
- } else {
+ if (threshold > 0) {
+ if (accept_pair((*s)[i].score, (*s)[j].score, threshold))
+ training.push_back(make_pair((*s)[i], (*s)[j]));
+ } else {
+ if((*s)[i].score != (*s)[j].score)
training.push_back(make_pair((*s)[i], (*s)[j]));
- }
}
}
}
@@ -83,7 +79,6 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor
bool
_PRO_cmp_pair_by_diff(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b)
{
- // descending order
return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score));
}
inline void
diff --git a/dtrain/score.cc b/dtrain/score.cc
index d0f9e8a0..b09d32ba 100644
--- a/dtrain/score.cc
+++ b/dtrain/score.cc
@@ -18,11 +18,15 @@ BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref
{
if (hyp_len == 0 || ref_len == 0) return 0.;
unsigned M = N_;
- if (ref_len < N_) M = ref_len;
+ vector<score_t> v = w_;
+ if (ref_len < N_) {
+ M = ref_len;
+ for (unsigned i = 0; i < M; i++) v[i] = 1./((score_t)M);
+ }
score_t sum = 0;
for (unsigned i = 0; i < M; i++) {
if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.;
- sum += w_[i] * log((score_t)counts.clipped_[i]/counts.sum_[i]);
+ sum += v[i] * log((score_t)counts.clipped_[i]/counts.sum_[i]);
}
return brevity_penalty(hyp_len, ref_len) * exp(sum);
}
@@ -55,12 +59,16 @@ StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref,
if (hyp_len == 0 || ref_len == 0) return 0.;
NgramCounts counts = make_ngram_counts(hyp, ref, N_);
unsigned M = N_;
- if (ref_len < N_) M = ref_len;
+ vector<score_t> v = w_;
+ if (ref_len < N_) {
+ M = ref_len;
+ for (unsigned i = 0; i < M; i++) v[i] = 1./((score_t)M);
+ }
score_t sum = 0, add = 0;
for (unsigned i = 0; i < M; i++) {
if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.;
if (i == 1) add = 1;
- sum += w_[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
+ sum += v[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add)));
}
return brevity_penalty(hyp_len, ref_len) * exp(sum);
}
diff --git a/dtrain/score.h b/dtrain/score.h
index d0e79f65..eb8ad912 100644
--- a/dtrain/score.h
+++ b/dtrain/score.h
@@ -61,8 +61,8 @@ struct NgramCounts
{
unsigned i;
for (i = 0; i < N_; i++) {
- clipped_[i] = 0;
- sum_[i] = 0;
+ clipped_[i] = 0.;
+ sum_[i] = 0.;
}
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 12:02:23 +0000