diff options
Diffstat (limited to 'dtrain')
28 files changed, 51 insertions, 1014 deletions
diff --git a/dtrain/NEXT b/dtrain/NEXT deleted file mode 100644 index eccfb313..00000000 --- a/dtrain/NEXT +++ /dev/null @@ -1,7 +0,0 @@ -make svm faster (cuda)? - other learning frameworks -target side rule ngram feature template -decoder meta-parameters test -sa-extract -> leave-one-out? -rerank while sgd? - diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc index e7a1244c..cf913765 100644 --- a/dtrain/dtrain.cc +++ b/dtrain/dtrain.cc @@ -15,7 +15,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use") ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration") ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id") - ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)") + ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)") ("k", po::value<unsigned>()->default_value(100), "how many translations to sample") ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'") ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'") @@ -47,7 +47,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) po::store(po::parse_config_file(ini_f, ini), *cfg); } po::notify(*cfg); - if (!cfg->count("decoder_config")) { + if (!cfg->count("decoder_config")) { cerr << cl << endl; return false; } @@ -93,10 +93,10 @@ main(int argc, char** argv) { // handle most parameters po::variables_map cfg; - if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong + if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong bool quiet = false; if (cfg.count("quiet")) quiet = true; - bool verbose = false; + bool verbose = false; if (cfg.count("verbose")) verbose = true; bool noup = false; if (cfg.count("noup")) noup = true; @@ -118,7 +118,7 @@ main(int argc, char** argv) inc_correct = true; const unsigned k = cfg["k"].as<unsigned>(); - const unsigned N = cfg["N"].as<unsigned>(); + const unsigned N = cfg["N"].as<unsigned>(); const unsigned T = cfg["epochs"].as<unsigned>(); const unsigned stop_after = cfg["stop_after"].as<unsigned>(); const string filter_type = cfg["filter"].as<string>(); @@ -241,7 +241,7 @@ main(int argc, char** argv) cerr << setw(25) << "rescale " << rescale << endl; cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; cerr << setw(25) << "input " << "'" << input_fn << "'" << endl; -#ifdef DTRAIN_LOCAL +#ifdef DTRAIN_LOCAL cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl; #endif cerr << setw(25) << "output " << "'" << output_fn << "'" << endl; @@ -258,7 +258,7 @@ main(int argc, char** argv) if (hstreaming) cerr << "reporter:status:Iteration #" << t+1 << " of " << T << endl; - time_t start, end; + time_t start, end; time(&start); #ifndef DTRAIN_LOCAL igzstream grammar_buf_in; @@ -281,7 +281,7 @@ main(int argc, char** argv) } // stop after X sentences (but still go on for those) if (stop_after > 0 && stop_after == ii && !next) stop = true; - + // produce some pretty output if (!quiet && !verbose) { if (ii == 0) cerr << " "; @@ -302,7 +302,7 @@ main(int argc, char** argv) } } } - + // next iteration if (next || stop) break; @@ -315,7 +315,7 @@ main(int argc, char** argv) vector<string> in_split; // input: sid\tsrc\tref\tpsg if (t == 0) { // handling input - split_in(in, in_split); + split_in(in, in_split); if (hstreaming && ii == 0) cerr << "reporter:counter:" << task_id << ",First ID," << in_split[0] << endl; // getting reference vector<string> ref_tok; @@ -369,13 +369,13 @@ main(int argc, char** argv) ref_ids = ref_ids_buf[ii]; } observer->SetRef(ref_ids); - if (t == 0) + if (t == 0) decoder.Decode(in, observer); else decoder.Decode(src_str_buf[ii], observer); #endif - // get (scored) samples + // get (scored) samples vector<ScoredHyp>* samples = observer->GetSamples(); if (verbose) { @@ -475,7 +475,7 @@ main(int argc, char** argv) } if (rescale) lambdas /= lambdas.l2norm(); - + ++ii; if (hstreaming) { @@ -485,7 +485,7 @@ main(int argc, char** argv) } // input loop - if (average) w_average += lambdas; + if (average) w_average += lambdas; if (scorer_str == "approx_bleu") scorer->Reset(); @@ -517,7 +517,7 @@ main(int argc, char** argv) score_diff = score_avg; model_diff = model_avg; } - + unsigned nonz = 0; if (!quiet || hstreaming) nonz = (unsigned)lambdas.size_nonzero(); @@ -543,12 +543,12 @@ main(int argc, char** argv) } if (hstreaming) { - 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)); - rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz); + 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)); + 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); } @@ -575,7 +575,7 @@ main(int argc, char** argv) if (select_weights == "best" || keep) { lambdas.init_vector(&dense_weights); string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz"; - Weights::WriteToFile(w_fn, dense_weights, true); + Weights::WriteToFile(w_fn, dense_weights, true); } } // outer loop @@ -625,7 +625,7 @@ main(int argc, char** argv) if (output_fn == "-" && hstreaming) cout << "__SHARD_COUNT__\t1" << endl; if (!quiet) cerr << "done" << endl; } - + if (!quiet) { cerr << _p5 << _np << endl << "---" << endl << "Best iteration: "; cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl; diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h index 61d60657..ac13995a 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########" @@ -49,7 +49,7 @@ inline void split_in(string& s, vector<string>& parts) unsigned e = f; f = s.find("\t", f+1); if (e != 0) parts.push_back(s.substr(e+1, f-e-1)); - else parts.push_back(s.substr(0, f)); + else parts.push_back(s.substr(0, f)); } s.erase(0, f+1); } diff --git a/dtrain/hstreaming/avg.rb b/dtrain/hstreaming/avg.rb index 5deb62e4..2599c732 100755 --- a/dtrain/hstreaming/avg.rb +++ b/dtrain/hstreaming/avg.rb @@ -1,4 +1,5 @@ #!/usr/bin/env ruby +# first arg may be an int of custom shard count shard_count_key = "__SHARD_COUNT__" @@ -22,7 +23,6 @@ else end w.each_key { |k| if k == shard_count_key - #puts "# shard count: #{shard_count.to_i}" next else puts "#{k}\t#{w[k]/shard_count}" diff --git a/dtrain/hstreaming/cdec.ini b/dtrain/hstreaming/cdec.ini index 61f13e86..d4f5cecd 100644 --- a/dtrain/hstreaming/cdec.ini +++ b/dtrain/hstreaming/cdec.ini @@ -2,11 +2,12 @@ formalism=scfg add_pass_through_rules=true scfg_max_span_limit=15 intersection_strategy=cube_pruning -cubepruning_pop_limit=200 +cubepruning_pop_limit=30 feature_function=WordPenalty feature_function=KLanguageModel nc-wmt11.en.srilm.gz #feature_function=ArityPenalty #feature_function=CMR2008ReorderingFeatures +#feature_function=Dwarf #feature_function=InputIndicator #feature_function=LexNullJump #feature_function=NewJump diff --git a/dtrain/hstreaming/dtrain.ini b/dtrain/hstreaming/dtrain.ini index 118a27c5..05535299 100644 --- a/dtrain/hstreaming/dtrain.ini +++ b/dtrain/hstreaming/dtrain.ini @@ -2,11 +2,11 @@ input=- output=- decoder_config=cdec.ini tmp=/var/hadoop/mapred/local/ -epochs=10 +epochs=1 k=100 N=4 learning_rate=0.0001 -gamma=0.00001 +gamma=0 scorer=stupid_bleu sample_from=kbest filter=uniq diff --git a/dtrain/hstreaming/dtrain.sh b/dtrain/hstreaming/dtrain.sh index ea0276dd..877ff94c 100755 --- a/dtrain/hstreaming/dtrain.sh +++ b/dtrain/hstreaming/dtrain.sh @@ -1,8 +1,9 @@ #!/bin/bash +# script to run dtrain with a task id pushd . &>/dev/null cd .. ID=$(basename $(pwd)) # attempt_... popd &>/dev/null -./dtrain -c dtrain.ini --hstreaming $ID +./dtrain -c dtrain.ini --hstreaming $ID diff --git a/dtrain/hstreaming/hadoop-streaming-job.sh b/dtrain/hstreaming/hadoop-streaming-job.sh index 90c2b790..92419956 100755 --- a/dtrain/hstreaming/hadoop-streaming-job.sh +++ b/dtrain/hstreaming/hadoop-streaming-job.sh @@ -6,17 +6,16 @@ EXP=a_simple_test HADOOP_HOME=/usr/lib/hadoop-0.20 JAR=contrib/streaming/hadoop-streaming-0.20.2-cdh3u1.jar HSTREAMING="$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/$JAR" -# ^^^ IN=input_on_hdfs OUT=output_weights_on_hdfs -# you can remove the -reducer line if you want to +# you can -reducer to NONE if you want to # do feature selection/averaging locally (e.g. to -# keep weights of the iterations) +# keep weights of all epochs) $HSTREAMING \ -mapper "dtrain.sh" \ - -reducer "lplp.rb l2 select_k 100000" \ + -reducer "ruby lplp.rb l2 select_k 100000" \ -input $IN \ -output $OUT \ -file dtrain.sh \ diff --git a/dtrain/hstreaming/lplp.rb b/dtrain/hstreaming/lplp.rb index 57353adb..f0cd58c5 100755 --- a/dtrain/hstreaming/lplp.rb +++ b/dtrain/hstreaming/lplp.rb @@ -29,7 +29,7 @@ end # selection def select_k(weights, norm_fun, n, k=10000) weights.sort{|a,b| norm_fun.call(b[1], n) <=> norm_fun.call(a[1], n)}.each { |p| - puts "#{p[0]}\t#{mean(p[1], n)}" + puts "#{p[0]}\t#{mean(p[1], n)}" k -= 1 if k == 0 then break end } diff --git a/dtrain/hstreaming/rule_count/map.sh b/dtrain/hstreaming/rule_count/map.sh deleted file mode 100755 index ae75fece..00000000 --- a/dtrain/hstreaming/rule_count/map.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -ruby rulecount.rb | sort | ruby red.rb - diff --git a/dtrain/hstreaming/rule_count/red.rb b/dtrain/hstreaming/rule_count/red.rb deleted file mode 100644 index 874ae7ac..00000000 --- a/dtrain/hstreaming/rule_count/red.rb +++ /dev/null @@ -1,24 +0,0 @@ -#!/usr/bin/env ruby - -STDIN.set_encoding 'utf-8' -STDOUT.set_encoding 'utf-8' - -def output(key, val) - puts "#{key}\t#{val}" -end - -prev_key = nil -sum = 0 -while line = STDIN.gets - key, val = line.strip.split /\t/ - if key != prev_key && sum > 0 - output prev_key, sum - prev_key = key - sum = 0 - elsif !prev_key - prev_key = key - end - sum += val.to_i -end -output prev_key, sum - diff --git a/dtrain/hstreaming/rule_count/rulecount.rb b/dtrain/hstreaming/rule_count/rulecount.rb deleted file mode 100644 index 67361fa4..00000000 --- a/dtrain/hstreaming/rule_count/rulecount.rb +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env ruby - -STDIN.set_encoding 'utf-8' -STDOUT.set_encoding 'utf-8' - -while line = STDIN.gets - a = line.strip.chomp.split "\t" - a[3..a.size].each { |r| - id = r.split("|||")[0..2].join("|||").to_s.strip.gsub("\s", "_") - puts "#{id}\t1" - } -end - diff --git a/dtrain/hstreaming/rule_count/test b/dtrain/hstreaming/rule_count/test deleted file mode 100644 index acd00a5e..00000000 --- a/dtrain/hstreaming/rule_count/test +++ /dev/null @@ -1,8 +0,0 @@ -a 1 -a 1 -a 1 -b 1 -b 1 -c 1 -d 1 -a 1 diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h index 0c2da994..bcd82610 100644 --- a/dtrain/kbestget.h +++ b/dtrain/kbestget.h @@ -59,7 +59,7 @@ struct HypSampler : public DecoderObserver vector<WordID>* ref_; virtual vector<ScoredHyp>* GetSamples()=0; inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; } - inline void SetRef(vector<WordID>& ref) { ref_ = &ref; } + inline void SetRef(vector<WordID>& ref) { ref_ = &ref; } }; //////////////////////////////////////////////////////////////////////////////// diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h index c45c8f64..eb4813ab 100644 --- a/dtrain/ksampler.h +++ b/dtrain/ksampler.h @@ -35,7 +35,7 @@ struct KSampler : public HypSampler ScoredHyp h; h.w = samples[i].words; h.f = samples[i].fmap; - h.model = log(samples[i].model_score); + h.model = log(samples[i].model_score); h.rank = i; h.score = scorer_->Score(h.w, *ref_, i); s_.push_back(h); diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h index 1fc5b8a0..93c0630a 100644 --- a/dtrain/pairsampling.h +++ b/dtrain/pairsampling.h @@ -46,6 +46,7 @@ part108010(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, s unsigned sz = s->size(); unsigned slice = 10; unsigned sep = sz%slice; + cout << "sep " << sep <<endl; if (sep == 0) sep = sz/slice; for (unsigned i = 0; i < sep; i++) { for (unsigned j = sep; j < sz; j++) { @@ -107,7 +108,7 @@ PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, } if (training.size() > 50) { sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff); - training.erase(training.begin()+50, training.end()); + training.erase(training.begin()+50, training.end()); } return; } diff --git a/dtrain/score.h b/dtrain/score.h index 85cd0317..5aceb81f 100644 --- a/dtrain/score.h +++ b/dtrain/score.h @@ -15,7 +15,7 @@ struct NgramCounts map<unsigned, unsigned> clipped; map<unsigned, unsigned> sum; - NgramCounts(const unsigned N) : N_(N) { Zero(); } + NgramCounts(const unsigned N) : N_(N) { Zero(); } inline void operator+=(const NgramCounts& rhs) diff --git a/dtrain/test/example/cdec.ini b/dtrain/test/example/cdec.ini index fe5ca759..6642107f 100644 --- a/dtrain/test/example/cdec.ini +++ b/dtrain/test/example/cdec.ini @@ -5,7 +5,8 @@ intersection_strategy=cube_pruning cubepruning_pop_limit=30 feature_function=WordPenalty feature_function=KLanguageModel test/example/nc-wmt11.en.srilm.gz -# all currently working feature function for translation: +# all currently working feature functions for translation: +# (with those features active that were used in the ACL paper) #feature_function=ArityPenalty #feature_function=CMR2008ReorderingFeatures #feature_function=Dwarf @@ -21,4 +22,3 @@ feature_function=RuleShape #feature_function=SourceSpanSizeFeatures #feature_function=SourceWordPenalty #feature_function=SpanFeatures -# ^^^ features active that were used in the ACL paper diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini index 66be6bf2..b59250f3 100644 --- a/dtrain/test/example/dtrain.ini +++ b/dtrain/test/example/dtrain.ini @@ -1,18 +1,18 @@ -input=test/example/nc-wmt11.1k.gz # use '-' for stdin +input=test/example/nc-wmt11.1k.gz # use '-' for STDIN output=weights.gz # a weights file (add .gz for gzip compression) or STDOUT '-' decoder_config=test/example/cdec.ini # config for cdec # weights for these features will be printed on each iteration print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough tmp=/tmp -stop_after=100 # stop epoch after 10 inputs +stop_after=100 # stop epoch after 100 inputs # interesting stuff -epochs=100 # run over input 3 times +epochs=3 # run over input 3 times k=100 # use 100best lists -N=4 # optimize (approx) BLEU4 +N=4 # optimize (approx) BLEU4 +scorer=stupid_bleu # use 'stupid' BLEU+1 learning_rate=0.0001 # learning rate -gamma=0 # use SVM reg -scorer=smooth_bleu # use smooth BLEU of (Liang et al. '06) +gamma=0 # use SVM reg sample_from=kbest # use kbest lists (as opposed to forest) filter=uniq # only unique entries in kbest (surface form) pair_sampling=108010 # 10 vs 80 vs 10 and 80 vs 10 diff --git a/dtrain/test/mtm11/logreg_cd/bin_class.cc b/dtrain/test/mtm11/logreg_cd/bin_class.cc deleted file mode 100644 index 19bcde25..00000000 --- a/dtrain/test/mtm11/logreg_cd/bin_class.cc +++ /dev/null @@ -1,4 +0,0 @@ -#include "bin_class.h" - -Objective::~Objective() {} - diff --git a/dtrain/test/mtm11/logreg_cd/bin_class.h b/dtrain/test/mtm11/logreg_cd/bin_class.h deleted file mode 100644 index 3466109a..00000000 --- a/dtrain/test/mtm11/logreg_cd/bin_class.h +++ /dev/null @@ -1,22 +0,0 @@ -#ifndef _BIN_CLASS_H_ -#define _BIN_CLASS_H_ - -#include <vector> -#include "sparse_vector.h" - -struct TrainingInstance { - // TODO add other info? loss for MIRA-type updates? - SparseVector<double> x_feature_map; - bool y; -}; - -struct Objective { - virtual ~Objective(); - - // returns f(x) and f'(x) - virtual double ObjectiveAndGradient(const SparseVector<double>& x, - const std::vector<TrainingInstance>& training_instances, - SparseVector<double>* g) const = 0; -}; - -#endif diff --git a/dtrain/test/mtm11/logreg_cd/log_reg.cc b/dtrain/test/mtm11/logreg_cd/log_reg.cc deleted file mode 100644 index ec2331fe..00000000 --- a/dtrain/test/mtm11/logreg_cd/log_reg.cc +++ /dev/null @@ -1,39 +0,0 @@ -#include "log_reg.h" - -#include <vector> -#include <cmath> - -#include "sparse_vector.h" - -using namespace std; - -double LogisticRegression::ObjectiveAndGradient(const SparseVector<double>& x, - const vector<TrainingInstance>& training_instances, - SparseVector<double>* g) const { - double cll = 0; - for (int i = 0; i < training_instances.size(); ++i) { - const double dotprod = training_instances[i].x_feature_map.dot(x); // TODO no bias, if bias, add x[0] - double lp_false = dotprod; - double lp_true = -dotprod; - if (0 < lp_true) { - lp_true += log1p(exp(-lp_true)); - lp_false = log1p(exp(lp_false)); - } else { - lp_true = log1p(exp(lp_true)); - lp_false += log1p(exp(-lp_false)); - } - lp_true *= -1; - lp_false *= -1; - if (training_instances[i].y) { // true label - cll -= lp_true; - (*g) -= training_instances[i].x_feature_map * exp(lp_false); - // (*g)[0] -= exp(lp_false); // bias - } else { // false label - cll -= lp_false; - (*g) += training_instances[i].x_feature_map * exp(lp_true); - // g += corpus[i].second * exp(lp_true); - } - } - return cll; -} - diff --git a/dtrain/test/mtm11/logreg_cd/log_reg.h b/dtrain/test/mtm11/logreg_cd/log_reg.h deleted file mode 100644 index ecc560b8..00000000 --- a/dtrain/test/mtm11/logreg_cd/log_reg.h +++ /dev/null @@ -1,14 +0,0 @@ -#ifndef _LOG_REG_H_ -#define _LOG_REG_H_ - -#include <vector> -#include "sparse_vector.h" -#include "bin_class.h" - -struct LogisticRegression : public Objective { - double ObjectiveAndGradient(const SparseVector<double>& x, - const std::vector<TrainingInstance>& training_instances, - SparseVector<double>* g) const; -}; - -#endif diff --git a/dtrain/test/mtm11/mira_update/Hildreth.cpp b/dtrain/test/mtm11/mira_update/Hildreth.cpp deleted file mode 100644 index 0e67eb15..00000000 --- a/dtrain/test/mtm11/mira_update/Hildreth.cpp +++ /dev/null @@ -1,187 +0,0 @@ -#include "Hildreth.h" -#include "sparse_vector.h" - -using namespace std; - -namespace Mira { - vector<double> Hildreth::optimise (vector< SparseVector<double> >& a, vector<double>& b) { - - size_t i; - int max_iter = 10000; - double eps = 0.00000001; - double zero = 0.000000000001; - - vector<double> alpha ( b.size() ); - vector<double> F ( b.size() ); - vector<double> kkt ( b.size() ); - - double max_kkt = -1e100; - - size_t K = b.size(); - - double A[K][K]; - bool is_computed[K]; - for ( i = 0; i < K; i++ ) - { - A[i][i] = a[i].dot(a[i]); - is_computed[i] = false; - } - - int max_kkt_i = -1; - - - for ( i = 0; i < b.size(); i++ ) - { - F[i] = b[i]; - kkt[i] = F[i]; - if ( kkt[i] > max_kkt ) - { - max_kkt = kkt[i]; - max_kkt_i = i; - } - } - - int iter = 0; - double diff_alpha; - double try_alpha; - double add_alpha; - - while ( max_kkt >= eps && iter < max_iter ) - { - - diff_alpha = A[max_kkt_i][max_kkt_i] <= zero ? 0.0 : F[max_kkt_i]/A[max_kkt_i][max_kkt_i]; - try_alpha = alpha[max_kkt_i] + diff_alpha; - add_alpha = 0.0; - - if ( try_alpha < 0.0 ) - add_alpha = -1.0 * alpha[max_kkt_i]; - else - add_alpha = diff_alpha; - - alpha[max_kkt_i] = alpha[max_kkt_i] + add_alpha; - - if ( !is_computed[max_kkt_i] ) - { - for ( i = 0; i < K; i++ ) - { - A[i][max_kkt_i] = a[i].dot(a[max_kkt_i] ); // for version 1 - //A[i][max_kkt_i] = 0; // for version 1 - is_computed[max_kkt_i] = true; - } - } - - for ( i = 0; i < F.size(); i++ ) - { - F[i] -= add_alpha * A[i][max_kkt_i]; - kkt[i] = F[i]; - if ( alpha[i] > zero ) - kkt[i] = abs ( F[i] ); - } - max_kkt = -1e100; - max_kkt_i = -1; - for ( i = 0; i < F.size(); i++ ) - if ( kkt[i] > max_kkt ) - { - max_kkt = kkt[i]; - max_kkt_i = i; - } - - iter++; - } - - return alpha; - } - - vector<double> Hildreth::optimise (vector< SparseVector<double> >& a, vector<double>& b, double C) { - - size_t i; - int max_iter = 10000; - double eps = 0.00000001; - double zero = 0.000000000001; - - vector<double> alpha ( b.size() ); - vector<double> F ( b.size() ); - vector<double> kkt ( b.size() ); - - double max_kkt = -1e100; - - size_t K = b.size(); - - double A[K][K]; - bool is_computed[K]; - for ( i = 0; i < K; i++ ) - { - A[i][i] = a[i].dot(a[i]); - is_computed[i] = false; - } - - int max_kkt_i = -1; - - - for ( i = 0; i < b.size(); i++ ) - { - F[i] = b[i]; - kkt[i] = F[i]; - if ( kkt[i] > max_kkt ) - { - max_kkt = kkt[i]; - max_kkt_i = i; - } - } - - int iter = 0; - double diff_alpha; - double try_alpha; - double add_alpha; - - while ( max_kkt >= eps && iter < max_iter ) - { - - diff_alpha = A[max_kkt_i][max_kkt_i] <= zero ? 0.0 : F[max_kkt_i]/A[max_kkt_i][max_kkt_i]; - try_alpha = alpha[max_kkt_i] + diff_alpha; - add_alpha = 0.0; - - if ( try_alpha < 0.0 ) - add_alpha = -1.0 * alpha[max_kkt_i]; - else if (try_alpha > C) - add_alpha = C - alpha[max_kkt_i]; - else - add_alpha = diff_alpha; - - alpha[max_kkt_i] = alpha[max_kkt_i] + add_alpha; - - if ( !is_computed[max_kkt_i] ) - { - for ( i = 0; i < K; i++ ) - { - A[i][max_kkt_i] = a[i].dot(a[max_kkt_i] ); // for version 1 - //A[i][max_kkt_i] = 0; // for version 1 - is_computed[max_kkt_i] = true; - } - } - - for ( i = 0; i < F.size(); i++ ) - { - F[i] -= add_alpha * A[i][max_kkt_i]; - kkt[i] = F[i]; - if (alpha[i] > C - zero) - kkt[i]=-kkt[i]; - else if (alpha[i] > zero) - kkt[i] = abs(F[i]); - - } - max_kkt = -1e100; - max_kkt_i = -1; - for ( i = 0; i < F.size(); i++ ) - if ( kkt[i] > max_kkt ) - { - max_kkt = kkt[i]; - max_kkt_i = i; - } - - iter++; - } - - return alpha; - } -} diff --git a/dtrain/test/mtm11/mira_update/Hildreth.h b/dtrain/test/mtm11/mira_update/Hildreth.h deleted file mode 100644 index 8d791085..00000000 --- a/dtrain/test/mtm11/mira_update/Hildreth.h +++ /dev/null @@ -1,10 +0,0 @@ -#include "sparse_vector.h" - -namespace Mira { - class Hildreth { - public : - static std::vector<double> optimise(std::vector< SparseVector<double> >& a, std::vector<double>& b); - static std::vector<double> optimise(std::vector< SparseVector<double> >& a, std::vector<double>& b, double C); - }; -} - diff --git a/dtrain/test/mtm11/mira_update/dtrain.cc b/dtrain/test/mtm11/mira_update/dtrain.cc deleted file mode 100644 index 933417a4..00000000 --- a/dtrain/test/mtm11/mira_update/dtrain.cc +++ /dev/null @@ -1,532 +0,0 @@ -#include "common.h" -#include "kbestget.h" -#include "util.h" -#include "sample.h" -#include "Hildreth.h" - -#include "ksampler.h" - -// boost compression -#include <boost/iostreams/device/file.hpp> -#include <boost/iostreams/filtering_stream.hpp> -#include <boost/iostreams/filter/gzip.hpp> -//#include <boost/iostreams/filter/zlib.hpp> -//#include <boost/iostreams/filter/bzip2.hpp> -using namespace boost::iostreams; - - -#ifdef DTRAIN_DEBUG -#include "tests.h" -#endif - - -/* - * init - * - */ -bool -init(int argc, char** argv, po::variables_map* cfg) -{ - po::options_description conff( "Configuration File Options" ); - size_t k, N, T, stop, n_pairs; - string s, f, update_type; - conff.add_options() - ( "decoder_config", po::value<string>(), "configuration file for cdec" ) - ( "kbest", po::value<size_t>(&k)->default_value(DTRAIN_DEFAULT_K), "k for kbest" ) - ( "ngrams", po::value<size_t>(&N)->default_value(DTRAIN_DEFAULT_N), "N for Ngrams" ) - ( "filter", po::value<string>(&f)->default_value("unique"), "filter kbest list" ) - ( "epochs", po::value<size_t>(&T)->default_value(DTRAIN_DEFAULT_T), "# of iterations T" ) - ( "input", po::value<string>(), "input file" ) - ( "scorer", po::value<string>(&s)->default_value(DTRAIN_DEFAULT_SCORER), "scoring metric" ) - ( "output", po::value<string>(), "output weights file" ) - ( "stop_after", po::value<size_t>(&stop)->default_value(0), "stop after X input sentences" ) - ( "weights_file", po::value<string>(), "input weights file (e.g. from previous iteration)" ) - ( "wprint", po::value<string>(), "weights to print on each iteration" ) - ( "noup", po::value<bool>()->zero_tokens(), "do not update weights" ); - - po::options_description clo("Command Line Options"); - clo.add_options() - ( "config,c", po::value<string>(), "dtrain config file" ) - ( "quiet,q", po::value<bool>()->zero_tokens(), "be quiet" ) - ( "update-type", po::value<string>(&update_type)->default_value("mira"), "perceptron or mira" ) - ( "n-pairs", po::value<size_t>(&n_pairs)->default_value(10), "number of pairs used to compute update" ) - ( "verbose,v", po::value<bool>()->zero_tokens(), "be verbose" ) -#ifndef DTRAIN_DEBUG - ; -#else - ( "test", "run tests and exit"); -#endif - po::options_description config_options, cmdline_options; - - config_options.add(conff); - cmdline_options.add(clo); - cmdline_options.add(conff); - - po::store( parse_command_line(argc, argv, cmdline_options), *cfg ); - if ( cfg->count("config") ) { - ifstream config( (*cfg)["config"].as<string>().c_str() ); - po::store( po::parse_config_file(config, config_options), *cfg ); - } - po::notify(*cfg); - - if ( !cfg->count("decoder_config") || !cfg->count("input") ) { - cerr << cmdline_options << endl; - return false; - } - if ( cfg->count("noup") && cfg->count("decode") ) { - cerr << "You can't use 'noup' and 'decode' at once." << endl; - return false; - } - if ( cfg->count("filter") && (*cfg)["filter"].as<string>() != "unique" - && (*cfg)["filter"].as<string>() != "no" ) { - cerr << "Wrong 'filter' type: '" << (*cfg)["filter"].as<string>() << "'." << endl; - } - #ifdef DTRAIN_DEBUG - if ( !cfg->count("test") ) { - cerr << cmdline_options << endl; - return false; - } - #endif - return true; -} - - -// output formatting -ostream& _nopos( ostream& out ) { return out << resetiosflags( ios::showpos ); } -ostream& _pos( ostream& out ) { return out << setiosflags( ios::showpos ); } -ostream& _prec2( ostream& out ) { return out << setprecision(2); } -ostream& _prec5( ostream& out ) { return out << setprecision(5); } - - - - -/* - * dtrain - * - */ -int -main( int argc, char** argv ) -{ - cout << setprecision( 5 ); - // handle most parameters - po::variables_map cfg; - if ( ! init(argc, argv, &cfg) ) exit(1); // something is wrong -#ifdef DTRAIN_DEBUG - if ( cfg.count("test") ) run_tests(); // run tests and exit -#endif - bool quiet = false; - if ( cfg.count("quiet") ) quiet = true; - bool verbose = false; - if ( cfg.count("verbose") ) verbose = true; - bool noup = false; - if ( cfg.count("noup") ) noup = true; - const size_t k = cfg["kbest"].as<size_t>(); - const size_t N = cfg["ngrams"].as<size_t>(); - const size_t T = cfg["epochs"].as<size_t>(); - const size_t stop_after = cfg["stop_after"].as<size_t>(); - const string filter_type = cfg["filter"].as<string>(); - const string update_type = cfg["update-type"].as<string>(); - const size_t n_pairs = cfg["n-pairs"].as<size_t>(); - const string output_file = cfg["output"].as<string>(); - if ( !quiet ) { - cout << endl << "dtrain" << endl << "Parameters:" << endl; - cout << setw(25) << "k " << k << endl; - cout << setw(25) << "N " << N << endl; - cout << setw(25) << "T " << T << endl; - if ( cfg.count("stop-after") ) - cout << setw(25) << "stop_after " << stop_after << endl; - if ( cfg.count("weights") ) - cout << setw(25) << "weights " << cfg["weights"].as<string>() << endl; - cout << setw(25) << "input " << "'" << cfg["input"].as<string>() << "'" << endl; - cout << setw(25) << "filter " << "'" << filter_type << "'" << endl; - } - - vector<string> wprint; - if ( cfg.count("wprint") ) { - boost::split( wprint, cfg["wprint"].as<string>(), boost::is_any_of(" ") ); - } - - // setup decoder, observer - register_feature_functions(); - SetSilent(true); - ReadFile ini_rf( cfg["decoder_config"].as<string>() ); - if ( !quiet ) - cout << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; - Decoder decoder( ini_rf.stream() ); - //KBestGetter observer( k, filter_type ); - MT19937 rng; - KSampler observer( k, &rng ); - - // scoring metric/scorer - string scorer_str = cfg["scorer"].as<string>(); - double (*scorer)( NgramCounts&, const size_t, const size_t, size_t, vector<float> ); - if ( scorer_str == "bleu" ) { - scorer = &bleu; - } else if ( scorer_str == "stupid_bleu" ) { - scorer = &stupid_bleu; - } else if ( scorer_str == "smooth_bleu" ) { - scorer = &smooth_bleu; - } else if ( scorer_str == "approx_bleu" ) { - scorer = &approx_bleu; - } else { - cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl; - exit(1); - } - // for approx_bleu - NgramCounts global_counts( N ); // counts for 1 best translations - size_t global_hyp_len = 0; // sum hypothesis lengths - size_t global_ref_len = 0; // sum reference lengths - // this is all BLEU implmentations - vector<float> bleu_weights; // we leave this empty -> 1/N; TODO? - if ( !quiet ) cout << setw(26) << "scorer '" << scorer_str << "'" << endl << endl; - - // init weights - Weights weights; - if ( cfg.count("weights") ) weights.InitFromFile( cfg["weights"].as<string>() ); - SparseVector<double> lambdas; - weights.InitSparseVector( &lambdas ); - vector<double> dense_weights; - - // input - if ( !quiet && !verbose ) - cout << "(a dot represents " << DTRAIN_DOTS << " lines of input)" << endl; - string input_fn = cfg["input"].as<string>(); - ifstream input; - if ( input_fn != "-" ) input.open( input_fn.c_str() ); - string in; - vector<string> in_split; // input: src\tref\tpsg - vector<string> ref_tok; // tokenized reference - vector<WordID> ref_ids; // reference as vector of WordID - string grammar_str; - - // buffer input for t > 0 - vector<string> src_str_buf; // source strings, TODO? memory - vector<vector<WordID> > ref_ids_buf; // references as WordID vecs - filtering_ostream grammar_buf; // written to compressed file in /tmp - // this is for writing the grammar buffer file - grammar_buf.push( gzip_compressor() ); - char grammar_buf_tmp_fn[] = DTRAIN_TMP_DIR"/dtrain-grammars-XXXXXX"; - mkstemp( grammar_buf_tmp_fn ); - grammar_buf.push( file_sink(grammar_buf_tmp_fn, ios::binary | ios::trunc) ); - - size_t sid = 0, in_sz = 99999999; // sentence id, input size - double acc_1best_score = 0., acc_1best_model = 0.; - vector<vector<double> > scores_per_iter; - double max_score = 0.; - size_t best_t = 0; - bool next = false, stop = false; - double score = 0.; - size_t cand_len = 0; - double overall_time = 0.; - - // for the perceptron/SVM; TODO as params - double eta = 0.0005; - double gamma = 0.;//01; // -> SVM - lambdas.add_value( FD::Convert("__bias"), 0 ); - - // for random sampling - srand ( time(NULL) ); - - - for ( size_t t = 0; t < T; t++ ) // T epochs - { - - time_t start, end; - time( &start ); - - // actually, we need only need this if t > 0 FIXME - ifstream grammar_file( grammar_buf_tmp_fn, ios_base::in | ios_base::binary ); - filtering_istream grammar_buf_in; - grammar_buf_in.push( gzip_decompressor() ); - grammar_buf_in.push( grammar_file ); - - // reset average scores - acc_1best_score = acc_1best_model = 0.; - - // reset sentence counter - sid = 0; - - if ( !quiet ) cout << "Iteration #" << t+1 << " of " << T << "." << endl; - - while( true ) - { - - // get input from stdin or file - in.clear(); - next = stop = false; // next iteration, premature stop - if ( t == 0 ) { - if ( input_fn == "-" ) { - if ( !getline(cin, in) ) next = true; - } else { - if ( !getline(input, in) ) next = true; - } - } else { - if ( sid == in_sz ) next = true; // stop if we reach the end of our input - } - // stop after X sentences (but still iterate for those) - if ( stop_after > 0 && stop_after == sid && !next ) stop = true; - - // produce some pretty output - if ( !quiet && !verbose ) { - if ( sid == 0 ) cout << " "; - if ( (sid+1) % (DTRAIN_DOTS) == 0 ) { - cout << "."; - cout.flush(); - } - if ( (sid+1) % (20*DTRAIN_DOTS) == 0) { - cout << " " << sid+1 << endl; - if ( !next && !stop ) cout << " "; - } - if ( stop ) { - if ( sid % (20*DTRAIN_DOTS) != 0 ) cout << " " << sid << endl; - cout << "Stopping after " << stop_after << " input sentences." << endl; - } else { - if ( next ) { - if ( sid % (20*DTRAIN_DOTS) != 0 ) { - cout << " " << sid << endl; - } - } - } - } - - // next iteration - if ( next || stop ) break; - - // weights - dense_weights.clear(); - weights.InitFromVector( lambdas ); - weights.InitVector( &dense_weights ); - decoder.SetWeights( dense_weights ); - - if ( t == 0 ) { - // handling input - in_split.clear(); - boost::split( in_split, in, boost::is_any_of("\t") ); // in_split[0] is id - // getting reference - ref_tok.clear(); ref_ids.clear(); - boost::split( ref_tok, in_split[2], boost::is_any_of(" ") ); - register_and_convert( ref_tok, ref_ids ); - ref_ids_buf.push_back( ref_ids ); - // process and set grammar - bool broken_grammar = true; - for ( string::iterator ti = in_split[3].begin(); ti != in_split[3].end(); ti++ ) { - if ( !isspace(*ti) ) { - broken_grammar = false; - break; - } - } - if ( broken_grammar ) continue; - grammar_str = boost::replace_all_copy( in_split[3], " __NEXT__RULE__ ", "\n" ) + "\n"; // FIXME copy, __ - grammar_buf << grammar_str << DTRAIN_GRAMMAR_DELIM << endl; - decoder.SetSentenceGrammarFromString( grammar_str ); - // decode, kbest - src_str_buf.push_back( in_split[1] ); - decoder.Decode( in_split[1], &observer ); - } else { - // get buffered grammar - grammar_str.clear(); - int i = 1; - while ( true ) { - string g; - getline( grammar_buf_in, g ); - if ( g == DTRAIN_GRAMMAR_DELIM ) break; - grammar_str += g+"\n"; - i += 1; - } - decoder.SetSentenceGrammarFromString( grammar_str ); - // decode, kbest - decoder.Decode( src_str_buf[sid], &observer ); - } - - // get kbest list - KBestList* kb; - //if ( ) { // TODO get from forest - kb = observer.GetKBest(); - //} - - // scoring kbest - if ( t > 0 ) ref_ids = ref_ids_buf[sid]; - for ( size_t i = 0; i < kb->GetSize(); i++ ) { - NgramCounts counts = make_ngram_counts( ref_ids, kb->sents[i], N ); - // this is for approx bleu - if ( scorer_str == "approx_bleu" ) { - if ( i == 0 ) { // 'context of 1best translations' - global_counts += counts; - global_hyp_len += kb->sents[i].size(); - global_ref_len += ref_ids.size(); - counts.reset(); - cand_len = 0; - } else { - cand_len = kb->sents[i].size(); - } - NgramCounts counts_tmp = global_counts + counts; - // TODO as param - score = 0.9 * scorer( counts_tmp, - global_ref_len, - global_hyp_len + cand_len, N, bleu_weights ); - } else { - // other scorers - cand_len = kb->sents[i].size(); - score = scorer( counts, - ref_ids.size(), - kb->sents[i].size(), N, bleu_weights ); - } - - kb->scores.push_back( score ); - - if ( i == 0 ) { - acc_1best_score += score; - acc_1best_model += kb->model_scores[i]; - } - - if ( verbose ) { - if ( i == 0 ) cout << "'" << TD::GetString( ref_ids ) << "' [ref]" << endl; - cout << _prec5 << _nopos << "[hyp " << i << "] " << "'" << TD::GetString( kb->sents[i] ) << "'"; - cout << " [SCORE=" << score << ",model="<< kb->model_scores[i] << "]" << endl; - cout << kb->feats[i] << endl; // this is maybe too verbose - } - } // Nbest loop - - if ( verbose ) cout << endl; - - - // UPDATE WEIGHTS - if ( !noup ) { - - TrainingInstances pairs; - sample_all( kb, pairs, n_pairs ); - - vector< SparseVector<double> > featureValueDiffs; - vector<double> lossMinusModelScoreDiffs; - for ( TrainingInstances::iterator ti = pairs.begin(); - ti != pairs.end(); ti++ ) { - - SparseVector<double> dv; - if ( ti->first_score - ti->second_score < 0 ) { - dv = ti->second - ti->first; - dv.add_value( FD::Convert("__bias"), -1 ); - - featureValueDiffs.push_back(dv); - double lossMinusModelScoreDiff = ti->loss_diff - ti->model_score_diff; - lossMinusModelScoreDiffs.push_back(lossMinusModelScoreDiff); - - if (update_type == "perceptron") { - lambdas += dv * eta; - cerr << "after perceptron update: " << lambdas << endl << endl; - } - - if ( verbose ) { - cout << "{{ f("<< ti->first_rank <<") > f(" << ti->second_rank << ") but g(i)="<< ti->first_score <<" < g(j)="<< ti->second_score << " so update" << endl; - cout << " i " << TD::GetString(kb->sents[ti->first_rank]) << endl; - cout << " " << kb->feats[ti->first_rank] << endl; - cout << " j " << TD::GetString(kb->sents[ti->second_rank]) << endl; - cout << " " << kb->feats[ti->second_rank] << endl; - cout << " diff vec: " << dv << endl; - cout << " lambdas after update: " << lambdas << endl; - cout << "}}" << endl; - } - } else { - //SparseVector<double> reg; - //reg = lambdas * ( 2 * gamma ); - //lambdas += reg * ( -eta ); - } - } - cerr << "Collected " << featureValueDiffs.size() << " constraints." << endl; - - double slack = 0.01; - if (update_type == "mira") { - if (featureValueDiffs.size() > 0) { - vector<double> alphas; - if (slack != 0) { - alphas = Mira::Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs, slack); - } else { - alphas = Mira::Hildreth::optimise(featureValueDiffs, lossMinusModelScoreDiffs); - } - - for (size_t k = 0; k < featureValueDiffs.size(); ++k) { - lambdas += featureValueDiffs[k] * alphas[k]; - } - // cerr << "after mira update: " << lambdas << endl << endl; - } - } - } - - ++sid; - - } // input loop - - if ( t == 0 ) in_sz = sid; // remember size (lines) of input - - // print some stats - double avg_1best_score = acc_1best_score/(double)in_sz; - double avg_1best_model = acc_1best_model/(double)in_sz; - double avg_1best_score_diff, avg_1best_model_diff; - if ( t > 0 ) { - avg_1best_score_diff = avg_1best_score - scores_per_iter[t-1][0]; - avg_1best_model_diff = avg_1best_model - scores_per_iter[t-1][1]; - } else { - avg_1best_score_diff = avg_1best_score; - avg_1best_model_diff = avg_1best_model; - } - cout << _prec5 << _pos << "WEIGHTS" << endl; - for (vector<string>::iterator it = wprint.begin(); it != wprint.end(); it++) { - cout << setw(16) << *it << " = " << dense_weights[FD::Convert( *it )] << endl; - } - - cout << " ---" << endl; - cout << _nopos << " avg score: " << avg_1best_score; - cout << _pos << " (" << avg_1best_score_diff << ")" << endl; - cout << _nopos << "avg model score: " << avg_1best_model; - cout << _pos << " (" << avg_1best_model_diff << ")" << endl; - vector<double> remember_scores; - remember_scores.push_back( avg_1best_score ); - remember_scores.push_back( avg_1best_model ); - scores_per_iter.push_back( remember_scores ); - if ( avg_1best_score > max_score ) { - max_score = avg_1best_score; - best_t = t; - } - - // close open files - if ( input_fn != "-" ) input.close(); - close( grammar_buf ); - grammar_file.close(); - - time ( &end ); - double time_dif = difftime( end, start ); - overall_time += time_dif; - if ( !quiet ) { - cout << _prec2 << _nopos << "(time " << time_dif/60. << " min, "; - cout << time_dif/(double)in_sz<< " s/S)" << endl; - } - - if ( t+1 != T ) cout << endl; - - if ( noup ) break; - - // write weights after every epoch - std::string s; - std::stringstream out; - out << t; - s = out.str(); - string weights_file = output_file + "." + s; - weights.WriteToFile(weights_file, true ); - - } // outer loop - - unlink( grammar_buf_tmp_fn ); - if ( !noup ) { - if ( !quiet ) cout << endl << "writing weights file '" << cfg["output"].as<string>() << "' ..."; - weights.WriteToFile( cfg["output"].as<string>(), true ); - if ( !quiet ) cout << "done" << endl; - } - - if ( !quiet ) { - cout << _prec5 << _nopos << endl << "---" << endl << "Best iteration: "; - cout << best_t+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl; - cout << _prec2 << "This took " << overall_time/60. << " min." << endl; - } - - return 0; -} - diff --git a/dtrain/test/mtm11/mira_update/sample.h b/dtrain/test/mtm11/mira_update/sample.h deleted file mode 100644 index 5c331bba..00000000 --- a/dtrain/test/mtm11/mira_update/sample.h +++ /dev/null @@ -1,101 +0,0 @@ -#ifndef _DTRAIN_SAMPLE_H_ -#define _DTRAIN_SAMPLE_H_ - - -#include "kbestget.h" - - -namespace dtrain -{ - - -struct TPair -{ - SparseVector<double> first, second; - size_t first_rank, second_rank; - double first_score, second_score; - double model_score_diff; - double loss_diff; -}; - -typedef vector<TPair> TrainingInstances; - - -void - sample_all( KBestList* kb, TrainingInstances &training, size_t n_pairs ) -{ - std::vector<double> loss_diffs; - TrainingInstances training_tmp; - for ( size_t i = 0; i < kb->GetSize()-1; i++ ) { - for ( size_t j = i+1; j < kb->GetSize(); j++ ) { - TPair p; - p.first = kb->feats[i]; - p.second = kb->feats[j]; - p.first_rank = i; - p.second_rank = j; - p.first_score = kb->scores[i]; - p.second_score = kb->scores[j]; - - bool conservative = 1; - if ( kb->scores[i] - kb->scores[j] < 0 ) { - // j=hope, i=fear - p.model_score_diff = kb->model_scores[j] - kb->model_scores[i]; - p.loss_diff = kb->scores[j] - kb->scores[i]; - training_tmp.push_back(p); - loss_diffs.push_back(p.loss_diff); - } - else if (!conservative) { - // i=hope, j=fear - p.model_score_diff = kb->model_scores[i] - kb->model_scores[j]; - p.loss_diff = kb->scores[i] - kb->scores[j]; - training_tmp.push_back(p); - loss_diffs.push_back(p.loss_diff); - } - } - } - - if (training_tmp.size() > 0) { - double threshold; - std::sort(loss_diffs.begin(), loss_diffs.end()); - std::reverse(loss_diffs.begin(), loss_diffs.end()); - threshold = loss_diffs.size() >= n_pairs ? loss_diffs[n_pairs-1] : loss_diffs[loss_diffs.size()-1]; - cerr << "threshold: " << threshold << endl; - size_t constraints = 0; - for (size_t i = 0; (i < training_tmp.size() && constraints < n_pairs); ++i) { - if (training_tmp[i].loss_diff >= threshold) { - training.push_back(training_tmp[i]); - constraints++; - } - } - } - else { - cerr << "No pairs selected." << endl; - } -} - -void -sample_rand( KBestList* kb, TrainingInstances &training ) -{ - srand( time(NULL) ); - for ( size_t i = 0; i < kb->GetSize()-1; i++ ) { - for ( size_t j = i+1; j < kb->GetSize(); j++ ) { - if ( rand() % 2 ) { - TPair p; - p.first = kb->feats[i]; - p.second = kb->feats[j]; - p.first_rank = i; - p.second_rank = j; - p.first_score = kb->scores[i]; - p.second_score = kb->scores[j]; - training.push_back( p ); - } - } - } -} - - -} // namespace - - -#endif - diff --git a/dtrain/test/toy/dtrain.ini b/dtrain/test/toy/dtrain.ini index abf22b94..a091732f 100644 --- a/dtrain/test/toy/dtrain.ini +++ b/dtrain/test/toy/dtrain.ini @@ -4,8 +4,8 @@ output=- print_weights=logp shell_rule house_rule small_rule little_rule PassThrough k=4 N=4 -epochs=3 -scorer=stupid_bleu +epochs=2 +scorer=bleu sample_from=kbest filter=uniq pair_sampling=all |