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-rw-r--r--dtrain/README.md125
-rw-r--r--dtrain/dtrain.cc107
-rw-r--r--dtrain/dtrain.h4
-rw-r--r--dtrain/hstreaming/cdec.ini3
-rwxr-xr-xdtrain/hstreaming/dtrain.sh2
-rw-r--r--dtrain/kbestget.h2
-rw-r--r--dtrain/pairsampling.h55
-rw-r--r--dtrain/test/example/cdec.ini5
-rw-r--r--dtrain/test/example/dtrain.ini24
-rw-r--r--dtrain/test/example/nc-1k.gzbin21474865 -> 0 bytes
-rw-r--r--dtrain/test/example/nc-wmt11.1k.gz (renamed from dtrain/test/example/nc-1k-tabs.gz)bin21185883 -> 21185883 bytes
-rw-r--r--dtrain/test/logreg_cd/bin_class.cc (renamed from dtrain/test/log_reg_dyer/bin_class.cc)0
-rw-r--r--dtrain/test/logreg_cd/bin_class.h (renamed from dtrain/test/log_reg_dyer/bin_class.h)0
-rw-r--r--dtrain/test/logreg_cd/log_reg.cc (renamed from dtrain/test/log_reg_dyer/log_reg.cc)0
-rw-r--r--dtrain/test/logreg_cd/log_reg.h (renamed from dtrain/test/log_reg_dyer/log_reg.h)0
-rw-r--r--dtrain/test/toy/dtrain.ini4
16 files changed, 229 insertions, 102 deletions
diff --git a/dtrain/README.md b/dtrain/README.md
index 46f783b0..c50f3cad 100644
--- a/dtrain/README.md
+++ b/dtrain/README.md
@@ -23,67 +23,60 @@ Ideas
-----
* *MULTIPARTITE* ranking (1 vs rest, cluster model/score)
* *REMEMBER* sampled translations (merge kbest lists)
-* *SELECT* iteration with highest real BLEU on devtest?
-* *GENERATED* data? (perfect translation always in kbest)
+* *SELECT* iteration with highest _real_ BLEU on devtest?
+* *SYNTHETIC* data? (perfect translation always in kbest)
* *CACHE* ngrams for scoring
-* hadoop *PIPES* imlementation
+* hadoop *PIPES* implementation
* *ITERATION* variants (shuffle resulting weights, re-iterate)
-* *MORE THAN ONE* reference for BLEU?
-* *RANDOM RESTARTS* or directions
+* *MORE THAN ONE* reference for BLEU, paraphrases?
+* *RANDOM RESTARTS* or random directions
* use separate *TEST SET* for each shard
* *REDUCE* training set (50k?)
* *SYNTAX* features (CD)
* distribute *DEV* set to all nodes, avg
-* *PARAPHRASES* for better approx BLEU?
-
-Uncertain, known bugs, problems
+Notes
-------------------------------
* cdec kbest vs 1best (no -k param), rescoring (ref?)? => ok(?)
-* no sparse vector in decoder => ok/fixed
-* PhraseModel features, mapping?
+* no sparse vector in decoder => fixed/'ok'
+* PhraseModel features 0..99, mapping?
* flex scanner jams on bad input, we could skip that
-* input/grammar caching (strings -> WordIDs)
-* look at forest sampling...
-* devtest loo or not? why loo grammars larger? (sort psgs | uniq -> grammar)
+* input/grammar caching (vector<string> -> vector<WordID>)
+* why loo grammars larger? are they? (sort psgs | uniq -> grammar)
* lower beam size to be faster?
* why is <unk> -100 in lm so good?
* noise helps for discriminative training?
* what does srilm do with -unk but nothing mapped to unk (<unk> unigram)?
=> this: http://www-speech.sri.com/pipermail/srilm-user/2007q4/000543.html
-* mira translation sampling? => done
-* does AER correlate with BLEU?
-
-random notes
-------------
-* learning rate tuned with perceptron
-* aer correlation with bleu?
-* dtrain (perc) used for some tests because no optimizer instability
+* does AER correlate with BLEU? paper?
+* learning rate tuned with perceptron?
+* dtrain (perceptron) used for some tests because no optimizer instability
* http://www.ark.cs.cmu.edu/cdyer/dtrain/
* repeat as often as max needed by any learner!
-* don't compare lms with diff vocab (stupid backoff paper)
-* what does mira/pro optimize?
-* early stopping
-* 10-20k rules per sent normal
-* shard size 500 -> 2k
-* giza vs. berkeleyaligner: giza less noise?
+* don't compare lms (perplex.) with diff vocab (see stupid backoff paper)
+* what does mira/pro optimize exactly?
+* early stopping (epsilon, no change in kbest list)
+* 10-20k rules per sent are normal
+* giza vs. berkeleyaligner: giza more/less noise?
* compound splitting -> more rules?
-* loo => ref can't be reached? (jackknifing)
+* loo (jackknifing) => ref can't be reached?
* prune singletons -> less noise? (do I do this?)
-* random sample: take 100 at random
+* random sample: take fixed X at random
+* scale of features/weights?
-features
+Features
--------
* baseline features (take whatever cdec implements for VEST)
* rule identifiers (feature name = rule as string)
* rule discounts (taken from frequency i or frequency interval [i,j] of rule in extraction from parallel training data) bins
+ => from PRO
* target ngrams (from nonterminals in rule rhs), with gaps?
* source-target unigrams (from word alignments used in rule extraction, if they are?)
* lhs, rhs, rule length features
* all other features depend on syntax annotation.
* word alignment
-FIXME, todo
+Todo
-----------
* merge dtrain part-X files, for better blocks (how to do this with 4.5tb ep)
* mapred count shard sents
@@ -114,7 +107,6 @@ FIXME, todo
* sample pairs like in pro
* mira forest sampling
-
Data
----
<pre>
@@ -274,3 +266,72 @@ loo vs non-loo? => generalization
train on dev, test on devtest
train on devtest, test on dev
as above ^^^
+
+
+ ---
+
+as PRO
+ - UPDATES: perceptron
+ - LEARNING RATE: 0.0005
+ - GAMMA: -
+ - #ITERATIONS: 30
+ - SCORER: stupid_bleu@4
+ - K: 100, 1500?(top X pairs)
+ - SAMPLE: kbest uniq, kbest no
+ - PAIR SAMPLING: all, PRO?TODO
+ - SELECT: best
+ - FEATURES: baseline, RuleShape+SpanFeatures
+ ---
+ - Note: no weight interpolation
+ no early stopping based on kbest lists (epsilon?TODO)
+
+dtrain tune reg
+ - updates: SVM
+ - pair sampling important!
+ - learning_rate= 100 50 10 5 1 0.5 0.1 0.05 0.01 0.005 0.001 0.0005 0.0001 0.00005 0.00001 0.000005 0.000001 0.0000005 0.0000001 0.0000000001
+
+ - gamma=
+
+ - scorer: stupid_bleu 3
+ - test weights: last
+ -
+ -
+ - test: devtest
+
+
+---
+weights visualization (blocks, color coded)
+zig zag!?
+repeat all basic exps with training set
+merge?
+
+
+
+
+--sample_from
+--k
+--filter
+--pair_sampling
+--N
+--epochs
+--scorer
+--learning_rate
+--gamma
+--select_weights
+[--unit_weight_vector]
+[--l1_reg]
+[--l1_reg_strength]
+
+---------
+corr best = really best?
+108010gaps
+
+coltrane: 9
+gillespie: 9
+staley: 2
+io: 6
+ioh: 4
+ slots
+
+
+when does overfitting begin?
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 0853173f..3d3aa2d3 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -6,32 +6,33 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
{
po::options_description ini("Configuration File Options");
ini.add_options()
- ("input", po::value<string>()->default_value("-"), "input file")
- ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
- ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
- ("decoder_config", po::value<string>(), "configuration file for cdec")
- ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: kbest, forest")
- ("k", po::value<unsigned>()->default_value(100), "how many translations to sample")
- ("filter", po::value<string>()->default_value("unique"), "filter kbest list: no, unique")
- ("pair_sampling", po::value<string>()->default_value("all"), "how to sample pairs: all, rand, 108010")
- ("N", po::value<unsigned>()->default_value(3), "N for Ngrams (BLEU)")
- ("epochs", po::value<unsigned>()->default_value(2), "# of iterations T (per shard)")
- ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_*, smooth_*, approx_*")
- ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences")
- ("print_weights", po::value<string>(), "weights to print on each iteration")
- ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id")
- ("learning_rate", po::value<weight_t>()->default_value(0.0005), "learning rate")
- ("gamma", po::value<weight_t>()->default_value(0), "gamma for SVM (0 for perceptron)")
- ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use")
- ("select_weights", po::value<string>()->default_value("last"), "output 'best' or 'last' weights ('VOID' to throw away)")
- ("keep_w", po::value<bool>()->zero_tokens(), "protocol weights for each iteration")
- ("unit_weight_vector", po::value<bool>()->zero_tokens(), "Rescale weight vector after each input")
- ("l1_reg", po::value<string>()->default_value("no"), "apply l1 regularization as in Tsuroka et al 2010")
- ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
+ ("input", po::value<string>()->default_value("-"), "input file")
+ ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT")
+ ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)")
+ ("decoder_config", po::value<string>(), "configuration file for cdec")
+ ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: kbest, forest")
+ ("k", po::value<unsigned>()->default_value(100), "how many translations to sample")
+ ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: no, uniq")
+ ("pair_sampling", po::value<string>()->default_value("all"), "how to sample pairs: all, 5050, 108010, PRO")
+ ("N", po::value<unsigned>()->default_value(3), "N for Ngrams (BLEU)")
+ ("epochs", po::value<unsigned>()->default_value(2), "# of iterations T (per shard)")
+ ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_*, smooth_*, approx_*")
+ ("learning_rate", po::value<weight_t>()->default_value(0.0005), "learning rate")
+ ("gamma", po::value<weight_t>()->default_value(0), "gamma for SVM (0 for perceptron)")
+ ("select_weights", po::value<string>()->default_value("last"), "output 'best' or 'last' weights ('VOID' to throw away)")
+ ("unit_wv", po::value<bool>()->zero_tokens(), "Rescale weight vector after each input")
+ ("l1_reg", po::value<string>()->default_value("no"), "apply l1 regularization as in Tsuroka et al 2010")
+ ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
+ ("update_ok", po::value<bool>()->zero_tokens(), "include correctly ranked pairs into updates")
+ ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences")
+ ("keep_w", po::value<bool>()->zero_tokens(), "keep weights files for each iteration")
+ ("print_weights", po::value<string>(), "weights to print on each iteration")
+ ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id")
+ ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use")
#ifdef DTRAIN_LOCAL
- ("refs,r", po::value<string>(), "references in local mode")
+ ("refs,r", po::value<string>(), "references in local mode")
#endif
- ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
+ ("noup", po::value<bool>()->zero_tokens(), "do not update weights");
po::options_description cl("Command Line Options");
cl.add_options()
("config,c", po::value<string>(), "dtrain config file")
@@ -63,13 +64,14 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl;
return false;
}
- if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "unique"
+ if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq"
&& (*cfg)["filter"].as<string>() != "no") {
- cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'unique' or 'no'." << endl;
+ cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'no'." << endl;
return false;
}
if ((*cfg)["pair_sampling"].as<string>() != "all"
- && (*cfg)["pair_sampling"].as<string>() != "rand" && (*cfg)["pair_sampling"].as<string>() != "108010") {
+ && (*cfg)["pair_sampling"].as<string>() != "5050" && (*cfg)["pair_sampling"].as<string>() != "108010"
+ && (*cfg)["pair_sampling"].as<string>() != "PRO") {
cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "', use 'all' or 'rand'." << endl;
return false;
}
@@ -101,11 +103,14 @@ main(int argc, char** argv)
task_id = cfg["hstreaming"].as<string>();
cerr.precision(17);
}
- bool unit_weight_vector = false;
- if (cfg.count("unit_weight_vector")) unit_weight_vector = true;
+ bool unit_wv = false;
+ if (cfg.count("unit_wv")) unit_wv = true;
HSReporter rep(task_id);
bool keep_w = false;
if (cfg.count("keep_w")) keep_w = true;
+ bool update_ok = false;
+ if (cfg.count("update_ok"))
+ update_ok = true;
const unsigned k = cfg["k"].as<unsigned>();
const unsigned N = cfg["N"].as<unsigned>();
@@ -118,7 +123,7 @@ main(int argc, char** argv)
vector<string> print_weights;
if (cfg.count("print_weights"))
boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" "));
-
+
// setup decoder
register_feature_functions();
SetSilent(true);
@@ -187,7 +192,7 @@ main(int argc, char** argv)
vector<vector<WordID> > ref_ids_buf; // references as WordID vecs
// where temp files go
string tmp_path = cfg["tmp"].as<string>();
- vector<string> w_tmp_files; // used for protocol_w
+ vector<string> w_tmp_files; // used for keep_w
#ifdef DTRAIN_LOCAL
string refs_fn = cfg["refs"].as<string>();
ReadFile refs(refs_fn);
@@ -226,6 +231,12 @@ main(int argc, char** argv)
cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl;
cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl;
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 (update_ok)
+ cerr << setw(25) << "up ok " << update_ok << endl;
+ if (unit_wv)
+ cerr << setw(25) << "unit weight vec " << unit_wv << endl;
if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl;
}
@@ -320,7 +331,7 @@ main(int argc, char** argv)
// get buffered grammar
string grammar_str;
while (true) {
- string rule;
+ string rule;
getline(grammar_buf_in, rule);
if (boost::starts_with(rule, DTRAIN_GRAMMAR_DELIM)) break;
grammar_str += rule + "\n";
@@ -372,13 +383,15 @@ main(int argc, char** argv)
if (!noup) {
vector<pair<ScoredHyp,ScoredHyp> > pairs;
if (pair_sampling == "all")
- sample_all_pairs(samples, pairs);
- if (pair_sampling == "rand")
- sample_rand_pairs(samples, pairs, &rng);
+ all_pairs(samples, pairs);
+ if (pair_sampling == "5050")
+ rand_pairs_5050(samples, pairs, &rng);
if (pair_sampling == "108010")
- sample108010(samples, pairs);
+ multpart108010(samples, pairs);
+ if (pair_sampling == "PRO")
+ PROsampling(samples, pairs);
npairs += 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;
@@ -388,6 +401,11 @@ main(int argc, char** argv)
SparseVector<weight_t> diff_vec = it->second.f - it->first.f;
lambdas.plus_eq_v_times_s(diff_vec, eta);
rank_errors++;
+ } else {
+ if (update_ok) {
+ 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 {
@@ -404,6 +422,8 @@ main(int argc, char** argv)
}
}
+ ////////
+ // TEST THIS
// reset cumulative_penalties after 1 iter?
// do this only once per INPUT (not per pair)
if (l1naive) {
@@ -439,8 +459,9 @@ main(int argc, char** argv)
}
}
}
+ ////////
- if (unit_weight_vector && sample_from == "forest") lambdas /= lambdas.l2norm();
+ if (unit_wv && sample_from == "forest") lambdas /= lambdas.l2norm();
++ii;
@@ -501,11 +522,11 @@ main(int argc, char** argv)
}
if (hstreaming) {
- rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*_SCALE));
- rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*_SCALE));
- rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*_SCALE));
- rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*_SCALE));
- rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*_SCALE));
+ rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*DTRAIN_SCALE));
+ rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*DTRAIN_SCALE));
+ rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*DTRAIN_SCALE));
+ rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*DTRAIN_SCALE));
+ rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*DTRAIN_SCALE));
unsigned nonz = (unsigned)lambdas.size_nonzero();
rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
rep.update_gcounter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz);
diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h
index f0d8fd45..3d76bd7f 100644
--- a/dtrain/dtrain.h
+++ b/dtrain/dtrain.h
@@ -1,8 +1,6 @@
#ifndef _DTRAIN_COMMON_H_
#define _DTRAIN_COMMON_H_
-
-
#include <iomanip>
#include <climits>
#include <string.h>
@@ -19,7 +17,7 @@
#define DTRAIN_DOTS 100 // when to display a '.'
#define DTRAIN_GRAMMAR_DELIM "########EOS########"
-#define _SCALE 100000
+#define DTRAIN_SCALE 100000
using namespace std;
using namespace dtrain;
diff --git a/dtrain/hstreaming/cdec.ini b/dtrain/hstreaming/cdec.ini
index bea54afe..5afa89a9 100644
--- a/dtrain/hstreaming/cdec.ini
+++ b/dtrain/hstreaming/cdec.ini
@@ -1,7 +1,8 @@
formalism=scfg
add_pass_through_rules=true
-cubepruning_pop_limit=30
scfg_max_span_limit=15
+intersection_strategy=cube_pruning
+cubepruning_pop_limit=200
feature_function=WordPenalty
feature_function=KLanguageModel nc-wmt11.en.srilm.3.gz
feature_function=RuleIdentityFeatures
diff --git a/dtrain/hstreaming/dtrain.sh b/dtrain/hstreaming/dtrain.sh
index 6d34012a..b6847591 100755
--- a/dtrain/hstreaming/dtrain.sh
+++ b/dtrain/hstreaming/dtrain.sh
@@ -2,7 +2,7 @@
pushd .
cd ..
-ID=$(basename $(pwd))
+ID=$(basename $(pwd)) # attempt_...
popd
./dtrain -c dtrain.ini --hstreaming $ID
diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h
index 88f8bc17..08104dec 100644
--- a/dtrain/kbestget.h
+++ b/dtrain/kbestget.h
@@ -86,7 +86,7 @@ struct KBestGetter : public HypSampler
void
KBestScored(const Hypergraph& forest)
{
- if (filter_type_ == "unique") {
+ if (filter_type_ == "uniq") {
KBestUnique(forest);
} else if (filter_type_ == "no") {
KBestNoFilter(forest);
diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h
index 131e90ca..4399dfee 100644
--- a/dtrain/pairsampling.h
+++ b/dtrain/pairsampling.h
@@ -6,7 +6,7 @@ namespace dtrain
inline void
-sample_all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training)
+all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training)
{
for (unsigned i = 0; i < s->size()-1; i++) {
for (unsigned j = i+1; j < s->size(); j++) {
@@ -19,7 +19,7 @@ sample_all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& train
}
inline void
-sample_rand_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training,
+rand_pairs_5050(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training,
MT19937* prng)
{
for (unsigned i = 0; i < s->size()-1; i++) {
@@ -35,15 +35,14 @@ sample_rand_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& trai
}
bool
-sort_samples_by_score(ScoredHyp a, ScoredHyp b)
+_multpart_cmp_hyp_by_score(ScoredHyp a, ScoredHyp b)
{
return a.score < b.score;
}
-
inline void
-sample108010(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training)
+multpart108010(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training)
{
- sort(s->begin(), s->end(), sort_samples_by_score);
+ sort(s->begin(), s->end(), _multpart_cmp_hyp_by_score);
pair<ScoredHyp,ScoredHyp> p;
unsigned sz = s->size();
unsigned slice = 10;
@@ -66,6 +65,50 @@ sample108010(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training)
}
+inline bool
+_PRO_accept_pair(pair<ScoredHyp,ScoredHyp> &p)
+{
+ if (fabs(p.first.score - p.second.score) < 0.05) return false;
+ return true;
+}
+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
+PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training) // ugly
+{
+ unsigned max_count = 5000, count = 0;
+ bool b = false;
+ //unsigned max_pairs = (s->size()*(s->size()-1))/2;
+ vector<pair<unsigned,unsigned> > taken;
+ for (unsigned i = 0; i < s->size()-1; i++) {
+ for (unsigned j = i+1; j < s->size(); j++) {
+ pair<ScoredHyp,ScoredHyp> p;
+ p.first = (*s)[i];
+ p.second = (*s)[j];
+ vector<pair<unsigned,unsigned> >::iterator it = find(taken.begin(), taken.end(), make_pair(i, j));
+ if (_PRO_accept_pair(p) && it == taken.end()) {
+ training.push_back(p);
+ count++;
+ taken.push_back(make_pair(i, j));
+ if (count == max_count) {
+ b = true;
+ break;
+ }
+ }
+ }
+ if (b) break;
+ }
+ sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff);
+ if (training.size() > 50)
+ training.erase(training.begin()+50, training.end());
+ return;
+}
+
+
} // namespace
#endif
diff --git a/dtrain/test/example/cdec.ini b/dtrain/test/example/cdec.ini
index 31a205c7..ff99de7b 100644
--- a/dtrain/test/example/cdec.ini
+++ b/dtrain/test/example/cdec.ini
@@ -1,7 +1,8 @@
formalism=scfg
add_pass_through_rules=true
-cubepruning_pop_limit=30
scfg_max_span_limit=15
+intersection_strategy=cube_pruning
+cubepruning_pop_limit=30
feature_function=WordPenalty
feature_function=KLanguageModel test/example/nc-wmt11.en.srilm.gz
-feature_function=RuleIdentityFeatures
+#feature_function=RuleIdentityFeatures
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index 0b066013..fab4d317 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -1,18 +1,20 @@
decoder_config=test/example/cdec.ini
k=100
N=3
-gamma=0.001
-epochs=20
-input=test/example/nc-1k-tabs.gz
-scorer=smooth_bleu
-output=- #weights.gz
-stop_after=5
+learning_rate=0.0005
+gamma=0
+epochs=3
+input=test/example/nc-wmt11.1k.gz
+output=-
+scorer=stupid_bleu
sample_from=forest
-pair_sampling=108010
-select_weights=VOID
+#filter=unique
+pair_sampling=5050
+select_weights=last
print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PassThrough
tmp=/tmp
-#unit_weight_vector=
-keep_w=true
+stop_after=10
+#keep_w=
+#update_ok=
#l1_reg=clip
-#l1_reg_strength=0.00001
+#l1_reg_strength=0.0001
diff --git a/dtrain/test/example/nc-1k.gz b/dtrain/test/example/nc-1k.gz
deleted file mode 100644
index f638a166..00000000
--- a/dtrain/test/example/nc-1k.gz
+++ /dev/null
Binary files differ
diff --git a/dtrain/test/example/nc-1k-tabs.gz b/dtrain/test/example/nc-wmt11.1k.gz
index 45496cd8..45496cd8 100644
--- a/dtrain/test/example/nc-1k-tabs.gz
+++ b/dtrain/test/example/nc-wmt11.1k.gz
Binary files differ
diff --git a/dtrain/test/log_reg_dyer/bin_class.cc b/dtrain/test/logreg_cd/bin_class.cc
index 19bcde25..19bcde25 100644
--- a/dtrain/test/log_reg_dyer/bin_class.cc
+++ b/dtrain/test/logreg_cd/bin_class.cc
diff --git a/dtrain/test/log_reg_dyer/bin_class.h b/dtrain/test/logreg_cd/bin_class.h
index 3466109a..3466109a 100644
--- a/dtrain/test/log_reg_dyer/bin_class.h
+++ b/dtrain/test/logreg_cd/bin_class.h
diff --git a/dtrain/test/log_reg_dyer/log_reg.cc b/dtrain/test/logreg_cd/log_reg.cc
index ec2331fe..ec2331fe 100644
--- a/dtrain/test/log_reg_dyer/log_reg.cc
+++ b/dtrain/test/logreg_cd/log_reg.cc
diff --git a/dtrain/test/log_reg_dyer/log_reg.h b/dtrain/test/logreg_cd/log_reg.h
index ecc560b8..ecc560b8 100644
--- a/dtrain/test/log_reg_dyer/log_reg.h
+++ b/dtrain/test/logreg_cd/log_reg.h
diff --git a/dtrain/test/toy/dtrain.ini b/dtrain/test/toy/dtrain.ini
index 5bfa5b2d..105c07df 100644
--- a/dtrain/test/toy/dtrain.ini
+++ b/dtrain/test/toy/dtrain.ini
@@ -3,7 +3,7 @@ k=4
N=3
epochs=2
input=test/toy/in
-scorer=stupid_bleu
-sample_from=forest
output=-
+scorer=stupid_bleu
+sample_from=kbest
print_weights=logp use_shell use_house PassThrough
generated by cgit v1.2.3 (git 2.46.0) at 2025-10-31 09:38:32 +0000