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-rw-r--r--minrisk/minrisk_optimize.cc67
-rw-r--r--training/Makefile.am1
-rw-r--r--training/entropy.cc41
-rw-r--r--training/entropy.h22
-rw-r--r--utils/fdict.h2
5 files changed, 124 insertions, 9 deletions
diff --git a/minrisk/minrisk_optimize.cc b/minrisk/minrisk_optimize.cc
index 6e651994..da8b5260 100644
--- a/minrisk/minrisk_optimize.cc
+++ b/minrisk/minrisk_optimize.cc
@@ -17,6 +17,7 @@
#include "ns_docscorer.h"
#include "candidate_set.h"
#include "risk.h"
+#include "entropy.h"
using namespace std;
namespace po = boost::program_options;
@@ -28,6 +29,9 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("weights,w",po::value<string>(), "[REQD] Weights files from current iterations")
("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
("evaluation_metric,m",po::value<string>()->default_value("IBM_BLEU"), "Evaluation metric (ibm_bleu, koehn_bleu, nist_bleu, ter, meteor, etc.)")
+ ("temperature,T",po::value<double>()->default_value(0.0), "Temperature parameter for objective (>0 increases the entropy)")
+ ("l1_strength,C",po::value<double>()->default_value(0.0), "L1 regularization strength")
+ ("memory_buffers,M",po::value<unsigned>()->default_value(20), "Memory buffers used in LBFGS")
("kbest_repository,R",po::value<string>(), "Accumulate k-best lists from previous iterations (parameter is path to repository)")
("kbest_size,k",po::value<unsigned>()->default_value(500u), "Top k-hypotheses to extract")
("help,h", "Help");
@@ -52,36 +56,80 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
EvaluationMetric* metric = NULL;
struct RiskObjective {
- explicit RiskObjective(const vector<training::CandidateSet>& tr) : training(tr) {}
+ explicit RiskObjective(const vector<training::CandidateSet>& tr, const double temp) : training(tr), T(temp) {}
double operator()(const vector<double>& x, double* g) const {
fill(g, g + x.size(), 0.0);
double obj = 0;
+ double h = 0;
for (unsigned i = 0; i < training.size(); ++i) {
training::CandidateSetRisk risk(training[i], *metric);
- SparseVector<double> tg;
+ training::CandidateSetEntropy entropy(training[i]);
+ SparseVector<double> tg, hg;
double r = risk(x, &tg);
+ double hh = entropy(x, &hg);
+ h += hh;
obj += r;
for (SparseVector<double>::iterator it = tg.begin(); it != tg.end(); ++it)
g[it->first] += it->second;
+ if (T) {
+ for (SparseVector<double>::iterator it = hg.begin(); it != hg.end(); ++it)
+ g[it->first] += T * it->second;
+ }
}
- cerr << (1-(obj / training.size())) << endl;
- return obj;
+ cerr << (1-(obj / training.size())) << " H=" << h << endl;
+ return obj - T * h;
}
const vector<training::CandidateSet>& training;
+ const double T; // temperature for entropy regularization
};
double LearnParameters(const vector<training::CandidateSet>& training,
+ const double temp, // > 0 increases the entropy, < 0 decreases the entropy
const double C1,
const unsigned memory_buffers,
vector<weight_t>* px) {
- RiskObjective obj(training);
+ RiskObjective obj(training, temp);
LBFGS<RiskObjective> lbfgs(px, obj, memory_buffers, C1);
lbfgs.MinimizeFunction();
return 0;
}
-// runs lines 4--15 of rampion algorithm
+#if 0
+struct FooLoss {
+ double operator()(const vector<double>& x, double* g) const {
+ fill(g, g + x.size(), 0.0);
+ training::CandidateSet cs;
+ training::CandidateSetEntropy cse(cs);
+ cs.cs.resize(3);
+ cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0);
+ cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0);
+ cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0);
+ cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5);
+ SparseVector<double> xx;
+ double h = cse(x, &xx);
+ cerr << cse(x, &xx) << endl; cerr << "G: " << xx << endl;
+ for (SparseVector<double>::iterator i = xx.begin(); i != xx.end(); ++i)
+ g[i->first] += i->second;
+ return -h;
+ }
+};
+#endif
+
int main(int argc, char** argv) {
+#if 0
+ training::CandidateSet cs;
+ training::CandidateSetEntropy cse(cs);
+ cs.cs.resize(3);
+ cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0);
+ cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0);
+ cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0);
+ cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5);
+ FooLoss foo;
+ vector<double> ww(FD::NumFeats()); ww[FD::Convert("F1")] = 1.0;
+ LBFGS<FooLoss> lbfgs(&ww, foo, 100, 0.0);
+ lbfgs.MinimizeFunction();
+ return 1;
+#endif
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const string evaluation_metric = conf["evaluation_metric"].as<string>();
@@ -89,8 +137,6 @@ int main(int argc, char** argv) {
metric = EvaluationMetric::Instance(evaluation_metric);
DocumentScorer ds(metric, conf["reference"].as<vector<string> >());
cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl;
- double goodsign = -1;
- double badsign = -goodsign;
Hypergraph hg;
string last_file;
@@ -141,7 +187,10 @@ int main(int argc, char** argv) {
cerr << "\nHypergraphs loaded.\n";
weights.resize(FD::NumFeats());
- LearnParameters(kis, 0.0, 100, &weights);
+ double c1 = conf["l1_strength"].as<double>();
+ double temp = conf["temperature"].as<double>();
+ unsigned m = conf["memory_buffers"].as<unsigned>();
+ LearnParameters(kis, temp, c1, m, &weights);
Weights::WriteToFile("-", weights);
return 0;
}
diff --git a/training/Makefile.am b/training/Makefile.am
index 68ebfab4..4cef0d5b 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -26,6 +26,7 @@ TESTS = lbfgs_test optimize_test
noinst_LIBRARIES = libtraining.a
libtraining_a_SOURCES = \
candidate_set.cc \
+ entropy.cc \
optimize.cc \
online_optimizer.cc \
risk.cc
diff --git a/training/entropy.cc b/training/entropy.cc
new file mode 100644
index 00000000..4fdbe2be
--- /dev/null
+++ b/training/entropy.cc
@@ -0,0 +1,41 @@
+#include "entropy.h"
+
+#include "prob.h"
+#include "candidate_set.h"
+
+using namespace std;
+
+namespace training {
+
+// see Mann and McCallum "Efficient Computation of Entropy Gradient ..." for
+// a mostly clear derivation of:
+// g = E[ F(x,y) * log p(y|x) ] + H(y | x) * E[ F(x,y) ]
+double CandidateSetEntropy::operator()(const vector<double>& params,
+ SparseVector<double>* g) const {
+ prob_t z;
+ vector<double> dps(cands_.size());
+ for (unsigned i = 0; i < cands_.size(); ++i) {
+ dps[i] = cands_[i].fmap.dot(params);
+ const prob_t u(dps[i], init_lnx());
+ z += u;
+ }
+ const double log_z = log(z);
+
+ SparseVector<double> exp_feats;
+ double entropy = 0;
+ for (unsigned i = 0; i < cands_.size(); ++i) {
+ const double log_prob = cands_[i].fmap.dot(params) - log_z;
+ const double prob = exp(log_prob);
+ const double e_logprob = prob * log_prob;
+ entropy -= e_logprob;
+ if (g) {
+ (*g) += cands_[i].fmap * e_logprob;
+ exp_feats += cands_[i].fmap * prob;
+ }
+ }
+ if (g) (*g) += exp_feats * entropy;
+ return entropy;
+}
+
+}
+
diff --git a/training/entropy.h b/training/entropy.h
new file mode 100644
index 00000000..796589ca
--- /dev/null
+++ b/training/entropy.h
@@ -0,0 +1,22 @@
+#ifndef _CSENTROPY_H_
+#define _CSENTROPY_H_
+
+#include <vector>
+#include "sparse_vector.h"
+
+namespace training {
+ class CandidateSet;
+
+ class CandidateSetEntropy {
+ public:
+ explicit CandidateSetEntropy(const CandidateSet& cs) : cands_(cs) {}
+ // compute the entropy (expected log likelihood) of a CandidateSet
+ // (optional) the gradient of the entropy with respect to params
+ double operator()(const std::vector<double>& params,
+ SparseVector<double>* g = NULL) const;
+ private:
+ const CandidateSet& cands_;
+ };
+};
+
+#endif
diff --git a/utils/fdict.h b/utils/fdict.h
index 71547d2e..eb853fb2 100644
--- a/utils/fdict.h
+++ b/utils/fdict.h
@@ -33,6 +33,8 @@ struct FD {
assert(dict_.max() == 0); // dictionary must not have
// been added to
hash_ = new PerfectHashFunction(cmph_file);
+#else
+ (void) cmph_file;
#endif
}
static inline int NumFeats() {
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-25 22:08:53 +0000