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-rw-r--r--training/mira/kbest_cut_mira.cc100
1 files changed, 57 insertions, 43 deletions
diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc
index e4435abb..990609d7 100644
--- a/training/mira/kbest_cut_mira.cc
+++ b/training/mira/kbest_cut_mira.cc
@@ -30,7 +30,6 @@
#include "sparse_vector.h"
using namespace std;
-using boost::shared_ptr;
namespace po = boost::program_options;
bool invert_score;
@@ -50,13 +49,6 @@ bool sent_approx;
bool checkloss;
bool stream;
-void SanityCheck(const vector<double>& w) {
- for (int i = 0; i < w.size(); ++i) {
- assert(!isnan(w[i]));
- assert(!isinf(w[i]));
- }
-}
-
struct FComp {
const vector<double>& w_;
FComp(const vector<double>& w) : w_(w) {}
@@ -149,7 +141,7 @@ struct HypothesisInfo {
double alpha;
double oracle_loss;
SparseVector<double> oracle_feat_diff;
- shared_ptr<HypothesisInfo> oracleN;
+ boost::shared_ptr<HypothesisInfo> oracleN;
};
bool ApproxEqual(double a, double b) {
@@ -157,7 +149,7 @@ bool ApproxEqual(double a, double b) {
return (fabs(a-b)/fabs(b)) < EPSILON;
}
-typedef shared_ptr<HypothesisInfo> HI;
+typedef boost::shared_ptr<HypothesisInfo> HI;
bool HypothesisCompareB(const HI& h1, const HI& h2 )
{
return h1->mt_metric > h2->mt_metric;
@@ -185,11 +177,11 @@ bool HypothesisCompareG(const HI& h1, const HI& h2 )
};
-void CuttingPlane(vector<shared_ptr<HypothesisInfo> >* cur_c, bool* again, vector<shared_ptr<HypothesisInfo> >& all_hyp, vector<weight_t> dense_weights)
+void CuttingPlane(vector<boost::shared_ptr<HypothesisInfo> >* cur_c, bool* again, vector<boost::shared_ptr<HypothesisInfo> >& all_hyp, vector<weight_t> dense_weights)
{
bool DEBUG_CUT = false;
- shared_ptr<HypothesisInfo> max_fear, max_fear_in_set;
- vector<shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c;
+ boost::shared_ptr<HypothesisInfo> max_fear, max_fear_in_set;
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c;
if(no_reweight)
{
@@ -235,9 +227,9 @@ void CuttingPlane(vector<shared_ptr<HypothesisInfo> >* cur_c, bool* again, vecto
}
-double ComputeDelta(vector<shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights )
+double ComputeDelta(vector<boost::shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights )
{
- vector<shared_ptr<HypothesisInfo> >& cur_pair = *cur_p;
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_pair = *cur_p;
double loss = cur_pair[0]->oracle_loss - cur_pair[1]->oracle_loss;
double margin = -(cur_pair[0]->oracleN->features.dot(dense_weights)- cur_pair[0]->features.dot(dense_weights)) + (cur_pair[1]->oracleN->features.dot(dense_weights) - cur_pair[1]->features.dot(dense_weights));
@@ -261,12 +253,12 @@ double ComputeDelta(vector<shared_ptr<HypothesisInfo> >* cur_p, double max_step_
}
-vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo> >* cur_c)
+vector<boost::shared_ptr<HypothesisInfo> > SelectPair(vector<boost::shared_ptr<HypothesisInfo> >* cur_c)
{
bool DEBUG_SELECT= false;
- vector<shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c;
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_constraint = *cur_c;
- vector<shared_ptr<HypothesisInfo> > pair;
+ vector<boost::shared_ptr<HypothesisInfo> > pair;
if (no_select || optimizer == 2){ //skip heuristic search and return oracle and fear for pa-mira
@@ -278,7 +270,7 @@ vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo>
for(int u=0;u != cur_constraint.size();u++)
{
- shared_ptr<HypothesisInfo> max_fear;
+ boost::shared_ptr<HypothesisInfo> max_fear;
if(DEBUG_SELECT) cerr<< "cur alpha " << u << " " << cur_constraint[u]->alpha;
for(int i=0; i < cur_constraint.size();i++) //select maximal violator
@@ -323,8 +315,8 @@ vector<shared_ptr<HypothesisInfo> > SelectPair(vector<shared_ptr<HypothesisInfo>
}
struct GoodBadOracle {
- vector<shared_ptr<HypothesisInfo> > good;
- vector<shared_ptr<HypothesisInfo> > bad;
+ vector<boost::shared_ptr<HypothesisInfo> > good;
+ vector<boost::shared_ptr<HypothesisInfo> > bad;
};
struct BasicObserver: public DecoderObserver {
@@ -367,8 +359,8 @@ struct TrainingObserver : public DecoderObserver {
const DocScorer& ds;
vector<ScoreP>& corpus_bleu_sent_stats;
vector<GoodBadOracle>& oracles;
- vector<shared_ptr<HypothesisInfo> > cur_best;
- shared_ptr<HypothesisInfo> cur_oracle;
+ vector<boost::shared_ptr<HypothesisInfo> > cur_best;
+ boost::shared_ptr<HypothesisInfo> cur_oracle;
const int kbest_size;
Hypergraph forest;
int cur_sent;
@@ -386,7 +378,7 @@ struct TrainingObserver : public DecoderObserver {
return *cur_best[0];
}
- const vector<shared_ptr<HypothesisInfo> > GetCurrentBest() const {
+ const vector<boost::shared_ptr<HypothesisInfo> > GetCurrentBest() const {
return cur_best;
}
@@ -411,8 +403,8 @@ struct TrainingObserver : public DecoderObserver {
}
- shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score, const vector<WordID>& hyp) {
- shared_ptr<HypothesisInfo> h(new HypothesisInfo);
+ boost::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score, const vector<WordID>& hyp) {
+ boost::shared_ptr<HypothesisInfo> h(new HypothesisInfo);
h->features = feats;
h->mt_metric = score;
h->hyp = hyp;
@@ -424,14 +416,14 @@ struct TrainingObserver : public DecoderObserver {
if (stream) sent_id = 0;
bool PRINT_LIST= false;
- vector<shared_ptr<HypothesisInfo> >& cur_good = oracles[sent_id].good;
- vector<shared_ptr<HypothesisInfo> >& cur_bad = oracles[sent_id].bad;
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_good = oracles[sent_id].good;
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_bad = oracles[sent_id].bad;
//TODO: look at keeping previous iterations hypothesis lists around
cur_best.clear();
cur_good.clear();
cur_bad.clear();
- vector<shared_ptr<HypothesisInfo> > all_hyp;
+ vector<boost::shared_ptr<HypothesisInfo> > all_hyp;
typedef KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,Filter> K;
K kbest(forest,kbest_size);
@@ -527,7 +519,7 @@ struct TrainingObserver : public DecoderObserver {
if(PRINT_LIST) { cerr << "GOOD" << endl; for(int u=0;u!=cur_good.size();u++) cerr << cur_good[u]->mt_metric << " " << cur_good[u]->hope << endl;}
//use hope for fear selection
- shared_ptr<HypothesisInfo>& oracleN = cur_good[0];
+ boost::shared_ptr<HypothesisInfo>& oracleN = cur_good[0];
if(fear_select == 1){ //compute fear hyps with model - bleu
if (PRINT_LIST) cerr << "FEAR " << endl;
@@ -663,13 +655,13 @@ int main(int argc, char** argv) {
invert_score = false;
}
- shared_ptr<DocScorer> ds;
+ boost::shared_ptr<DocScorer> ds;
//normal: load references, stream: start stream scorer
if (stream) {
- ds = shared_ptr<DocScorer>(new DocStreamScorer(type, vector<string>(0), ""));
+ ds = boost::shared_ptr<DocScorer>(new DocStreamScorer(type, vector<string>(0), ""));
cerr << "Scoring doc stream with " << metric_name << endl;
} else {
- ds = shared_ptr<DocScorer>(new DocScorer(type, conf["reference"].as<vector<string> >(), ""));
+ ds = boost::shared_ptr<DocScorer>(new DocScorer(type, conf["reference"].as<vector<string> >(), ""));
cerr << "Loaded " << ds->size() << " references for scoring with " << metric_name << endl;
}
vector<ScoreP> corpus_bleu_sent_stats;
@@ -734,12 +726,34 @@ int main(int argc, char** argv) {
ViterbiESentence(bobs.hypergraph[0], &trans);
cout << TD::GetString(trans) << endl;
continue;
- // Translate and update (normal MIRA)
+ // Special command:
+ // CMD ||| arg1 ||| arg2 ...
} else {
- ds->update(buf.substr(delim + 5));
- buf = buf.substr(0, delim);
+ string cmd = buf.substr(0, delim);
+ buf = buf.substr(delim + 5);
+ // Translate and update (normal MIRA)
+ // LEARN ||| source ||| reference
+ if (cmd == "LEARN") {
+ delim = buf.find(" ||| ");
+ ds->update(buf.substr(delim + 5));
+ buf = buf.substr(0, delim);
+ } else if (cmd == "WEIGHTS") {
+ // WEIGHTS ||| WRITE
+ if (buf == "WRITE") {
+ cout << Weights::GetString(dense_weights) << endl;
+ // WEIGHTS ||| f1=w1 f2=w2 ...
+ } else {
+ Weights::UpdateFromString(buf, dense_weights);
+ }
+ continue;
+ } else {
+ cerr << "Error: cannot parse command, skipping line:" << endl;
+ cerr << cmd << " ||| " << buf << endl;
+ continue;
+ }
}
}
+ // Regular mode or LEARN line from stream mode
//TODO: allow batch updating
lambdas.init_vector(&dense_weights);
dense_w_local = dense_weights;
@@ -752,9 +766,9 @@ int main(int argc, char** argv) {
const HypothesisInfo& cur_good = *oracles[cur_sent].good[0];
const HypothesisInfo& cur_bad = *oracles[cur_sent].bad[0];
- vector<shared_ptr<HypothesisInfo> >& cur_good_v = oracles[cur_sent].good;
- vector<shared_ptr<HypothesisInfo> >& cur_bad_v = oracles[cur_sent].bad;
- vector<shared_ptr<HypothesisInfo> > cur_best_v = observer.GetCurrentBest();
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_good_v = oracles[cur_sent].good;
+ vector<boost::shared_ptr<HypothesisInfo> >& cur_bad_v = oracles[cur_sent].bad;
+ vector<boost::shared_ptr<HypothesisInfo> > cur_best_v = observer.GetCurrentBest();
tot_loss += cur_hyp.mt_metric;
@@ -802,13 +816,13 @@ int main(int argc, char** argv) {
}
else if(optimizer == 5) //full mira with n-best list of constraints from hope, fear, model best
{
- vector<shared_ptr<HypothesisInfo> > cur_constraint;
+ vector<boost::shared_ptr<HypothesisInfo> > cur_constraint;
cur_constraint.insert(cur_constraint.begin(), cur_bad_v.begin(), cur_bad_v.end());
cur_constraint.insert(cur_constraint.begin(), cur_best_v.begin(), cur_best_v.end());
cur_constraint.insert(cur_constraint.begin(), cur_good_v.begin(), cur_good_v.end());
bool optimize_again;
- vector<shared_ptr<HypothesisInfo> > cur_pair;
+ vector<boost::shared_ptr<HypothesisInfo> > cur_pair;
//SMO
for(int u=0;u!=cur_constraint.size();u++)
cur_constraint[u]->alpha =0;
@@ -857,7 +871,7 @@ int main(int argc, char** argv) {
else if(optimizer == 2 || optimizer == 3) //PA and Cutting Plane MIRA update
{
bool DEBUG_SMO= true;
- vector<shared_ptr<HypothesisInfo> > cur_constraint;
+ vector<boost::shared_ptr<HypothesisInfo> > cur_constraint;
cur_constraint.push_back(cur_good_v[0]); //add oracle to constraint set
bool optimize_again = true;
int cut_plane_calls = 0;
@@ -897,7 +911,7 @@ int main(int argc, char** argv) {
while (iter < smo_iter)
{
//select pair to optimize from constraint set
- vector<shared_ptr<HypothesisInfo> > cur_pair = SelectPair(&cur_constraint);
+ vector<boost::shared_ptr<HypothesisInfo> > cur_pair = SelectPair(&cur_constraint);
if(cur_pair.empty()){
iter=MAX_SMO;
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-13 10:03:56 +0000