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authorChris Dyer <cdyer@cs.cmu.edu>2012-05-29 21:39:22 -0400
committerChris Dyer <cdyer@cs.cmu.edu>2012-05-29 21:39:22 -0400
commit317d650f6cb1e24ac6f3be6f7bf9d4246a59e0e5 (patch)
tree02a8217d6910453049d5a2b96d3214baf19737c5 /rampion/rampion_cccp.cc
parent6b7f7ae44228221b3de158035d190940ff677de1 (diff)
add support to rampion for accumulating k-best lists
Diffstat (limited to 'rampion/rampion_cccp.cc')
-rw-r--r--rampion/rampion_cccp.cc69
1 files changed, 39 insertions, 30 deletions
diff --git a/rampion/rampion_cccp.cc b/rampion/rampion_cccp.cc
index 7a6f1f0c..1e36dc51 100644
--- a/rampion/rampion_cccp.cc
+++ b/rampion/rampion_cccp.cc
@@ -14,6 +14,7 @@
#include "viterbi.h"
#include "ns.h"
#include "ns_docscorer.h"
+#include "candidate_set.h"
using namespace std;
namespace po = boost::program_options;
@@ -25,6 +26,7 @@ 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.)")
+ ("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")
("cccp_iterations,I", po::value<unsigned>()->default_value(10u), "CCCP iterations (T')")
("ssd_iterations,J", po::value<unsigned>()->default_value(5u), "Stochastic subgradient iterations (T'')")
@@ -50,38 +52,36 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
}
-struct HypInfo {
- HypInfo() : g(-100.0f) {}
- HypInfo(const vector<WordID>& h,
- const SparseVector<weight_t>& feats,
- const SegmentEvaluator& scorer, const EvaluationMetric* metric) : hyp(h), x(feats) {
- SufficientStats ss;
- scorer.Evaluate(hyp, &ss);
- g = metric->ComputeScore(ss);
+struct GainFunction {
+ explicit GainFunction(const EvaluationMetric* m) : metric(m) {}
+ float operator()(const SufficientStats& eval_feats) const {
+ float g = metric->ComputeScore(eval_feats);
if (!metric->IsErrorMetric()) g = 1 - g;
+ return g;
}
-
- vector<WordID> hyp;
- float g;
- SparseVector<weight_t> x;
+ const EvaluationMetric* metric;
};
-void CostAugmentedSearch(const vector<HypInfo>& kbest,
+template <typename GainFunc>
+void CostAugmentedSearch(const GainFunc& gain,
+ const training::CandidateSet& cs,
const SparseVector<double>& w,
double alpha,
SparseVector<double>* fmap) {
unsigned best_i = 0;
double best = -numeric_limits<double>::infinity();
- for (unsigned i = 0; i < kbest.size(); ++i) {
- double s = kbest[i].x.dot(w) + alpha * kbest[i].g;
+ for (unsigned i = 0; i < cs.size(); ++i) {
+ double s = cs[i].fmap.dot(w) + alpha * gain(cs[i].eval_feats);
if (s > best) {
best = s;
best_i = i;
}
}
- *fmap = kbest[best_i].x;
+ *fmap = cs[best_i].fmap;
}
+
+
// runs lines 4--15 of rampion algorithm
int main(int argc, char** argv) {
po::variables_map conf;
@@ -97,6 +97,11 @@ int main(int argc, char** argv) {
Hypergraph hg;
string last_file;
ReadFile in_read(conf["input"].as<string>());
+ string kbest_repo;
+ if (conf.count("kbest_repository")) {
+ kbest_repo = conf["kbest_repository"].as<string>();
+ MkDirP(kbest_repo);
+ }
istream &in=*in_read.stream();
const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
const unsigned tp = conf["cccp_iterations"].as<unsigned>();
@@ -112,40 +117,44 @@ int main(int argc, char** argv) {
Weights::InitSparseVector(vweights, &weights);
}
string line, file;
- vector<vector<HypInfo> > kis;
+ vector<training::CandidateSet> kis;
cerr << "Loading hypergraphs...\n";
while(getline(in, line)) {
istringstream is(line);
int sent_id;
kis.resize(kis.size() + 1);
- vector<HypInfo>& curkbest = kis.back();
+ training::CandidateSet& curkbest = kis.back();
+ string kbest_file;
+ if (kbest_repo.size()) {
+ ostringstream os;
+ os << kbest_repo << "/kbest." << sent_id << ".txt.gz";
+ kbest_file = os.str();
+ if (FileExists(kbest_file))
+ curkbest.ReadFromFile(kbest_file);
+ }
is >> file >> sent_id;
ReadFile rf(file);
if (kis.size() % 5 == 0) { cerr << '.'; }
if (kis.size() % 200 == 0) { cerr << " [" << kis.size() << "]\n"; }
HypergraphIO::ReadFromJSON(rf.stream(), &hg);
hg.Reweight(weights);
- KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
-
- for (int i = 0; i < kbest_size; ++i) {
- const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
- kbest.LazyKthBest(hg.nodes_.size() - 1, i);
- if (!d) break;
- curkbest.push_back(HypInfo(d->yield, d->feature_values, *ds[sent_id], metric));
- }
+ curkbest.AddKBestCandidates(hg, kbest_size, ds[sent_id]);
+ if (kbest_file.size())
+ curkbest.WriteToFile(kbest_file);
}
cerr << "\nHypergraphs loaded.\n";
vector<SparseVector<weight_t> > goals(kis.size()); // f(x_i,y+,h+)
SparseVector<weight_t> fear; // f(x,y-,h-)
+ const GainFunction gain(metric);
for (unsigned iterp = 1; iterp <= tp; ++iterp) {
cerr << "CCCP Iteration " << iterp << endl;
- for (int i = 0; i < goals.size(); ++i)
- CostAugmentedSearch(kis[i], weights, goodsign * alpha, &goals[i]);
+ for (unsigned i = 0; i < goals.size(); ++i)
+ CostAugmentedSearch(gain, kis[i], weights, goodsign * alpha, &goals[i]);
for (unsigned iterpp = 1; iterpp <= tpp; ++iterpp) {
cerr << " SSD Iteration " << iterpp << endl;
- for (int i = 0; i < goals.size(); ++i) {
- CostAugmentedSearch(kis[i], weights, badsign * alpha, &fear);
+ for (unsigned i = 0; i < goals.size(); ++i) {
+ CostAugmentedSearch(gain, kis[i], weights, badsign * alpha, &fear);
weights -= weights * (eta * reg / goals.size());
weights += (goals[i] - fear) * eta;
}