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-rw-r--r--dtrain/dtrain.cc595
1 files changed, 59 insertions, 536 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index 8464a429..95fc81af 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -1,33 +1,6 @@
-#include <sstream>
-#include <iostream>
-#include <vector>
-#include <cassert>
-#include <cmath>
+#include "dcommon.h"
-#include "config.h"
-#include <boost/shared_ptr.hpp>
-#include <boost/algorithm/string.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "sentence_metadata.h"
-#include "scorer.h"
-#include "verbose.h"
-#include "viterbi.h"
-#include "hg.h"
-#include "prob.h"
-#include "kbest.h"
-#include "ff_register.h"
-#include "decoder.h"
-#include "filelib.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-#include "sampler.h"
-
-using namespace std;
-namespace boostpo = boost::program_options;
/*
@@ -35,19 +8,19 @@ namespace boostpo = boost::program_options;
*
*/
bool
-init(int argc, char** argv, boostpo::variables_map* conf)
+init(int argc, char** argv, po::variables_map* conf)
{
- boostpo::options_description opts( "Options" );
+ po::options_description opts( "Options" );
opts.add_options()
- ( "decoder-config,c", boostpo::value<string>(), "configuration file for cdec" )
- ( "kbest,k", boostpo::value<size_t>(), "k for kbest" )
- ( "ngrams,n", boostpo::value<int>(), "n for Ngrams" )
- ( "filter,f", boostpo::value<string>(), "filter kbest list" )
+ ( "decoder-config,c", po::value<string>(), "configuration file for cdec" )
+ ( "kbest,k", po::value<size_t>(), "k for kbest" )
+ ( "ngrams,n", po::value<int>(), "n for Ngrams" )
+ ( "filter,f", po::value<string>(), "filter kbest list" )
( "test", "run tests and exit");
- boostpo::options_description cmdline_options;
+ po::options_description cmdline_options;
cmdline_options.add(opts);
- boostpo::store( parse_command_line(argc, argv, cmdline_options), *conf );
- boostpo::notify( *conf );
+ po::store( parse_command_line(argc, argv, cmdline_options), *conf );
+ po::notify( *conf );
if ( ! (conf->count("decoder-config") || conf->count("test")) ) {
cerr << cmdline_options << endl;
return false;
@@ -57,451 +30,14 @@ init(int argc, char** argv, boostpo::variables_map* conf)
/*
- * KBestGetter
- *
- */
-struct KBestList {
- vector<SparseVector<double> > feats;
- vector<vector<WordID> > sents;
- vector<double> scores;
-};
-struct KBestGetter : public DecoderObserver
-{
- KBestGetter( const size_t k ) : k_(k) {}
- const size_t k_;
- KBestList kb;
-
- virtual void
- NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
- {
- GetKBest(smeta.GetSentenceID(), *hg);
- }
-
- KBestList* getkb() { return &kb; }
-
- void
- GetKBest(int sent_id, const Hypergraph& forest)
- {
- kb.scores.clear();
- kb.sents.clear();
- kb.feats.clear();
- KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest( forest, k_ );
- for ( size_t i = 0; i < k_; ++i ) {
- const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
- kbest.LazyKthBest( forest.nodes_.size() - 1, i );
- if (!d) break;
- kb.sents.push_back( d->yield);
- kb.feats.push_back( d->feature_values );
- kb.scores.push_back( d->score );
- }
- }
-};
-
-
-/*
- * write_training_data_for_sofia
- *
- */
-void
-sofia_write_training_data()
-{
- // TODO
-}
-
-
-/*
- * call_sofia
- *
- */
-void
-sofia_call()
-{
- // TODO
-}
-
-
-/*
- * sofia_model2weights
- *
- */
-void
-sofia_read_model()
-{
- // TODO
-}
-
-
-/*
- * make_ngrams
- *
- */
-typedef map<vector<WordID>, size_t> Ngrams;
-Ngrams
-make_ngrams( vector<WordID>& s, size_t N )
-{
- Ngrams ngrams;
- vector<WordID> ng;
- for ( size_t i = 0; i < s.size(); i++ ) {
- ng.clear();
- for ( size_t j = i; j < min( i+N, s.size() ); j++ ) {
- ng.push_back( s[j] );
- ngrams[ng]++;
- }
- }
- return ngrams;
-}
-
-
-/*
- * NgramCounts
- *
- */
-struct NgramCounts
-{
- NgramCounts( const size_t N ) : N_( N ) {
- reset();
- }
- size_t N_;
- map<size_t, size_t> clipped;
- map<size_t, size_t> sum;
-
- void
- operator+=( const NgramCounts& rhs )
- {
- assert( N_ == rhs.N_ );
- for ( size_t i = 0; i < N_; i++ ) {
- this->clipped[i] += rhs.clipped.find(i)->second;
- this->sum[i] += rhs.sum.find(i)->second;
- }
- }
-
- void
- add( size_t count, size_t ref_count, size_t i )
- {
- assert( i < N_ );
- if ( count > ref_count ) {
- clipped[i] += ref_count;
- sum[i] += count;
- } else {
- clipped[i] += count;
- sum[i] += count;
- }
- }
-
- void
- reset()
- {
- size_t i;
- for ( i = 0; i < N_; i++ ) {
- clipped[i] = 0;
- sum[i] = 0;
- }
- }
-
- void
- print()
- {
- for ( size_t i = 0; i < N_; i++ ) {
- cout << i+1 << "grams (clipped):\t" << clipped[i] << endl;
- cout << i+1 << "grams:\t\t\t" << sum[i] << endl;
- }
- }
-};
-
-
-/*
- * ngram_matches
- *
- */
-NgramCounts
-make_ngram_counts( vector<WordID> hyp, vector<WordID> ref, size_t N )
-{
- Ngrams hyp_ngrams = make_ngrams( hyp, N );
- Ngrams ref_ngrams = make_ngrams( ref, N );
- NgramCounts counts( N );
- Ngrams::iterator it;
- Ngrams::iterator ti;
- for ( it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++ ) {
- ti = ref_ngrams.find( it->first );
- if ( ti != ref_ngrams.end() ) {
- counts.add( it->second, ti->second, it->first.size() - 1 );
- } else {
- counts.add( it->second, 0, it->first.size() - 1 );
- }
- }
- return counts;
-}
-
-
-/*
- * brevity_penaly
- *
- */
-double
-brevity_penaly( const size_t hyp_len, const size_t ref_len )
-{
- if ( hyp_len > ref_len ) return 1;
- return exp( 1 - (double)ref_len/(double)hyp_len );
-}
-
-
-/*
- * bleu
- * as in "BLEU: a Method for Automatic Evaluation of Machine Translation" (Papineni et al. '02)
- * page TODO
- * 0 if for N one of the counts = 0
- */
-double
-bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
- size_t N, vector<float> weights = vector<float>() )
-{
- if ( hyp_len == 0 || ref_len == 0 ) return 0;
- if ( ref_len < N ) N = ref_len;
- float N_ = (float)N;
- if ( weights.empty() )
- {
- for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
- }
- double sum = 0;
- for ( size_t i = 0; i < N; i++ ) {
- if ( counts.clipped[i] == 0 || counts.sum[i] == 0 ) return 0;
- sum += weights[i] * log( (double)counts.clipped[i] / (double)counts.sum[i] );
- }
- return brevity_penaly( hyp_len, ref_len ) * exp( sum );
-}
-
-
-/*
- * stupid_bleu
- * as in "ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation (Lin & Och '04)
- * page TODO
- * 0 iff no 1gram match
- */
-double
-stupid_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
- size_t N, vector<float> weights = vector<float>() )
-{
- if ( hyp_len == 0 || ref_len == 0 ) return 0;
- if ( ref_len < N ) N = ref_len;
- float N_ = (float)N;
- if ( weights.empty() )
- {
- for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
- }
- double sum = 0;
- float add = 0;
- for ( size_t i = 0; i < N; i++ ) {
- if ( i == 1 ) add = 1;
- sum += weights[i] * log( ((double)counts.clipped[i] + add) / ((double)counts.sum[i] + add) );
- }
- return brevity_penaly( hyp_len, ref_len ) * exp( sum );
-}
-
-
-/*
- * smooth_bleu
- * as in "An End-to-End Discriminative Approach to Machine Translation" (Liang et al. '06)
- * page TODO
- * max. 0.9375
- */
-double
-smooth_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
- const size_t N, vector<float> weights = vector<float>() )
-{
- if ( hyp_len == 0 || ref_len == 0 ) return 0;
- float N_ = (float)N;
- if ( weights.empty() )
- {
- for ( size_t i = 0; i < N; i++ ) weights.push_back( 1/N_ );
- }
- double sum = 0;
- float j = 1;
- for ( size_t i = 0; i < N; i++ ) {
- if ( counts.clipped[i] == 0 || counts.sum[i] == 0) continue;
- sum += exp((weights[i] * log((double)counts.clipped[i]/(double)counts.sum[i]))) / pow( 2, N_-j+1 );
- j++;
- }
- return brevity_penaly( hyp_len, ref_len ) * sum;
-}
-
-
-/*
- * approx_bleu
- * as in "Online Large-Margin Training for Statistical Machine Translation" (Watanabe et al. '07)
- * page TODO
- *
- */
-double
-approx_bleu( NgramCounts& counts, const size_t hyp_len, const size_t ref_len,
- const size_t N, vector<float> weights = vector<float>() )
-{
- return bleu( counts, hyp_len, ref_len, N, weights );
-}
-
-
-/*
- * register_and_convert
- *
- */
-void
-register_and_convert(const vector<string>& strs, vector<WordID>& ids)
-{
- vector<string>::const_iterator it;
- for ( it = strs.begin(); it < strs.end(); it++ ) {
- ids.push_back( TD::Convert( *it ) );
- }
-}
-
-
-/*
- *
- *
- */
-void
-test_ngrams()
-{
- cout << "Testing ngrams..." << endl << endl;
- size_t N = 5;
- cout << "N = " << N << endl;
- vector<int> a; // hyp
- vector<int> b; // ref
- cout << "a ";
- for (size_t i = 1; i <= 8; i++) {
- cout << i << " ";
- a.push_back(i);
- }
- cout << endl << "b ";
- for (size_t i = 1; i <= 4; i++) {
- cout << i << " ";
- b.push_back(i);
- }
- cout << endl << endl;
- NgramCounts c = make_ngram_counts( a, b, N );
- assert( c.clipped[N-1] == 0 );
- assert( c.sum[N-1] == 4 );
- c.print();
- c += c;
- cout << endl;
- c.print();
- cout << endl;
-}
-
-
-/*
- *
- *
- */
-double
-approx_equal( double x, double y )
-{
- const double EPSILON = 1E-5;
- if ( x == 0 ) return fabs( y ) <= EPSILON;
- if ( y == 0 ) return fabs( x ) <= EPSILON;
- return fabs( x - y ) / max( fabs(x), fabs(y) ) <= EPSILON;
-}
-
-
-/*
- *
- *
- */
-#include <boost/assign/std/vector.hpp>
-#include <iomanip>
-void
-test_metrics()
-{
- cout << "Testing metrics..." << endl << endl;
- using namespace boost::assign;
- vector<string> a, b;
- vector<double> expect_vanilla, expect_smooth, expect_stupid;
- a += "a a a a", "a a a a", "a", "a", "b", "a a a a", "a a", "a a a", "a b a"; // hyp
- b += "b b b b", "a a a a", "a", "b", "b b b b", "a", "a a", "a a a", "a b b"; // ref
- expect_vanilla += 0, 1, 1, 0, 0, .25, 1, 1, 0;
- expect_smooth += 0, .9375, .0625, 0, .00311169, .0441942, .1875, .4375, .161587;
- expect_stupid += 0, 1, 1, 0, .0497871, .25, 1, 1, .605707;
- vector<string> aa, bb;
- vector<WordID> aai, bbi;
- double vanilla, smooth, stupid;
- size_t N = 4;
- cout << "N = " << N << endl << endl;
- for ( size_t i = 0; i < a.size(); i++ ) {
- cout << " hyp: " << a[i] << endl;
- cout << " ref: " << b[i] << endl;
- aa.clear(); bb.clear(); aai.clear(); bbi.clear();
- boost::split( aa, a[i], boost::is_any_of(" ") );
- boost::split( bb, b[i], boost::is_any_of(" ") );
- register_and_convert( aa, aai );
- register_and_convert( bb, bbi );
- NgramCounts counts = make_ngram_counts( aai, bbi, N );
- vanilla = bleu( counts, aa.size(), bb.size(), N);
- smooth = smooth_bleu( counts, aa.size(), bb.size(), N);
- stupid = stupid_bleu( counts, aa.size(), bb.size(), N);
- assert( approx_equal(vanilla, expect_vanilla[i]) );
- assert( approx_equal(smooth, expect_smooth[i]) );
- assert( approx_equal(stupid, expect_stupid[i]) );
- cout << setw(14) << "bleu = " << vanilla << endl;
- cout << setw(14) << "smooth bleu = " << smooth << endl;
- cout << setw(14) << "stupid bleu = " << stupid << endl << endl;
- }
- cout << endl;
-}
-
-/*
- *
- *
- */
-void
-test_SetWeights()
-{
- cout << "Testing Weights::SetWeight..." << endl << endl;
- Weights weights;
- SparseVector<double> lambdas;
- weights.InitSparseVector( &lambdas );
- weights.SetWeight( &lambdas, "test", 0 );
- weights.SetWeight( &lambdas, "test1", 1 );
- WordID fid = FD::Convert( "test2" );
- weights.SetWeight( &lambdas, fid, 2 );
- string fn = "weights-test";
- cout << "FD::NumFeats() " << FD::NumFeats() << endl;
- assert( FD::NumFeats() == 4 );
- weights.WriteToFile( fn, true );
- cout << endl;
-}
-
-
-/*
- *
- *
- */
-void
-run_tests()
-{
- cout << endl;
- test_ngrams();
- cout << endl;
- test_metrics();
- cout << endl;
- test_SetWeights();
- exit(0);
-}
-
-
-void
-print_FD()
-{
- for ( size_t i = 0; i < FD::NumFeats(); i++ ) cout << FD::Convert(i)<< endl;
-}
-
-
-/*
* main
*
*/
int
main(int argc, char** argv)
{
- //SetSilent(true);
- boostpo::variables_map conf;
+ SetSilent(true);
+ po::variables_map conf;
if (!init(argc, argv, &conf)) return 1;
if ( conf.count("test") ) run_tests();
register_feature_functions();
@@ -509,7 +45,9 @@ main(int argc, char** argv)
ReadFile ini_rf(conf["decoder-config"].as<string>());
Decoder decoder(ini_rf.stream());
KBestGetter observer(k);
-
+ size_t N = 4; // TODO as parameter/in config
+
+ // TODO scoring metric as parameter/in config
// for approx. bleu
//NgramCounts global_counts;
//size_t global_hyp_len;
@@ -523,82 +61,67 @@ main(int argc, char** argv)
lambdas.set_value(FD::Convert("logp"), 0);
- vector<string> strs;
+ vector<string> strs, ref_strs;
+ vector<WordID> ref_ids;
string in, psg;
- size_t i = 0;
+ size_t sid = 0;
+ cerr << "(1 dot equals 100 lines of input)" << endl;
while( getline(cin, in) ) {
- if ( !SILENT ) cerr << endl << endl << "Getting kbest for sentence #" << i << endl;
- // why? why!?
+ //if ( !SILENT )
+ // cerr << endl << endl << "Getting kbest for sentence #" << sid << endl;
+ if ( (sid+1) % 100 == 0 ) {
+ cerr << ".";
+ if ( (sid+1)%1000 == 0 ) cerr << endl;
+ }
+ if ( sid > 5000 ) break;
+ // weights
dense_weights.clear();
weights.InitFromVector( lambdas );
weights.InitVector( &dense_weights );
decoder.SetWeights( dense_weights );
- //cout << "use_shell " << dense_weights[FD::Convert("use_shell")] << endl;
+ //if ( sid > 100 ) break;
+ // handling input..
strs.clear();
boost::split( strs, in, boost::is_any_of("\t") );
+ // grammar
psg = boost::replace_all_copy( strs[2], " __NEXT_RULE__ ", "\n" ); psg += "\n";
- //decoder.SetId(i);
decoder.SetSentenceGrammar( psg );
decoder.Decode( strs[0], &observer );
- KBestList* kb = observer.getkb();
+ KBestList* kb = observer.GetKBest();
+ // reference
+ ref_strs.clear(); ref_ids.clear();
+ boost::split( ref_strs, strs[1], boost::is_any_of(" ") );
+ register_and_convert( ref_strs, ref_ids );
+ // scoring kbest
+ double score = 0;
+ Scores scores;
for ( size_t i = 0; i < k; i++ ) {
- cout << i << " ";
- for (size_t j = 0; j < kb->sents[i].size(); ++j ) {
- cout << TD::Convert( kb->sents[i][j] ) << " ";
- }
- cout << kb->scores[i];
- cout << endl;
+ NgramCounts counts = make_ngram_counts( ref_ids, kb->sents[i], 4 );
+ score = smooth_bleu( counts,
+ ref_ids.size(),
+ kb->sents[i].size(), N );
+ ScorePair sp( kb->scores[i], score );
+ scores.push_back( sp );
+ //cout << "'" << TD::GetString( ref_ids ) << "' vs '" << TD::GetString( kb->sents[i] ) << "' SCORE=" << score << endl;
+ //cout << kb->feats[i] << endl;
}
- lambdas.set_value( FD::Convert("use_shell"), 1 );
- lambdas.set_value( FD::Convert("use_a"), 1 );
+ //cout << "###" << endl;
+ SofiaLearner learner;
+ learner.Init( sid, kb->feats, scores );
+ learner.Update(lambdas);
+ // initializing learner
+ // TODO
+ // updating weights
+ //lambdas.set_value( FD::Convert("use_shell"), 1 );
+ //lambdas.set_value( FD::Convert("use_a"), 1 );
//print_FD();
+ sid += 1; // TODO does cdec count this already?
}
-
+
weights.WriteToFile( "weights-final", true );
+
+ cerr << endl;
return 0;
}
- // next: FMap, ->sofia, ->FMap, -> Weights
- // learner gets all used features (binary! and dense (logprob is sum of logprobs!))
- // only for those feats with weight > 0 after learning
- // see decoder line 548
-
-
-/*
- * TODO
- * iterate over training set, for t=1..T
- * mapred impl
- * mapper: main
- * reducer: average weights, global NgramCounts for approx. bleu
- * 1st cut: hadoop streaming?
- * batch, non-batch in the mapper (what sofia gets, regenerated Kbest lists)
- * filter kbest yes/no
- * sofia: --eta_type explicit
- * psg preparation source\tref\tpsg
- * set reference for cdec?
- * LM
- * shared?
- * startup?
- * X reference(s) for *bleu!?
- * kbest nicer (do not iterate twice)!? -> shared_ptr
- * multipartite ranking
- * weights! global, per sentence from global, featuremap
- * const decl...
- * sketch: batch/iter options
- * weights.cc: why wv_?
- * --weights cmd line (for iterations): script to call again/hadoop streaming?
- * I do not need to remember features, cdec does
- * resocre hg?
- * do not use Decoder::Decode!?
- * what happens if feature not in FD? 0???
- */
-
-/*
- * PROBLEMS
- * cdec kbest vs 1best (no -k param)
- * FD, Weights::wv_ grow too large, see utils/weights.cc; decoder/hg.h; decoder/scfg_translator.cc; utils/fdict.cc!?
- * sparse vector instead of vector<double> for weights in Decoder?
- * PhraseModel_* features for psg!? (seem to be generated)
- */
-