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-rw-r--r--pro-train/mr_pro_map.cc118
1 files changed, 100 insertions, 18 deletions
diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
index b046cdea..128d93ce 100644
--- a/pro-train/mr_pro_map.cc
+++ b/pro-train/mr_pro_map.cc
@@ -10,6 +10,7 @@
#include "sampler.h"
#include "filelib.h"
#include "stringlib.h"
+#include "weights.h"
#include "scorer.h"
#include "inside_outside.h"
#include "hg_io.h"
@@ -27,10 +28,10 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("reference,r",po::value<vector<string> >(), "[REQD] Reference translation (tokenized text)")
- ("source,s",po::value<string>(), "Source file (ignored, except for AER)")
+ ("source,s",po::value<string>()->default_value(""), "Source file (ignored, except for AER)")
("loss_function,l",po::value<string>()->default_value("ibm_bleu"), "Loss function being optimized")
("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
- ("weights,w",po::value<string>(), "[REQD] Current weights file")
+ ("weights,w",po::value<vector<string> >(), "[REQD] Weights files from previous and current iterations")
("kbest_size,k",po::value<unsigned>()->default_value(1500u), "Top k-hypotheses to extract")
("candidate_pairs,G", po::value<unsigned>()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)")
("best_pairs,X", po::value<unsigned>()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)")
@@ -44,6 +45,10 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
cerr << "Please specify one or more references using -r <REF.TXT>\n";
flag = true;
}
+ if (!conf->count("weights")) {
+ cerr << "Please specify one or more weights using -w <WEIGHTS.TXT>\n";
+ flag = true;
+ }
if (flag || conf->count("help")) {
cerr << dcmdline_options << endl;
exit(1);
@@ -51,18 +56,78 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
}
struct HypInfo {
- HypInfo(const vector<WordID>& h, const SparseVector<double>& feats) : hyp(h), g_(-1), x(feats) {}
- double g() {
+ HypInfo(const vector<WordID>& h, const SparseVector<double>& feats) : hyp(h), g_(-100.0), x(feats) {}
+
+ // lazy evaluation
+ double g(const SentenceScorer& scorer) const {
+ if (g_ == -100.0)
+ g_ = scorer.ScoreCandidate(hyp)->ComputeScore();
return g_;
}
- private:
- int sent_id;
vector<WordID> hyp;
- double g_;
+ mutable double g_;
public:
SparseVector<double> x;
};
+struct ThresholdAlpha {
+ explicit ThresholdAlpha(double t = 0.05) : threshold(t) {}
+ double operator()(double mag) const {
+ if (mag < threshold) return 0.0; else return 1.0;
+ }
+ const double threshold;
+};
+
+struct TrainingInstance {
+ TrainingInstance(const SparseVector<double>& feats, bool positive, double diff) : x(feats), y(positive), gdiff(diff) {}
+ SparseVector<double> x;
+#ifdef DEBUGGING_PRO
+ vector<WordID> a;
+ vector<WordID> b;
+#endif
+ bool y;
+ double gdiff;
+};
+
+struct DiffOrder {
+ bool operator()(const TrainingInstance& a, const TrainingInstance& b) const {
+ return a.gdiff > b.gdiff;
+ }
+};
+
+template<typename Alpha>
+void Sample(const unsigned gamma, const unsigned xi, const vector<HypInfo>& J_i, const SentenceScorer& scorer, const Alpha& alpha_i, bool invert_score, vector<TrainingInstance>* pv) {
+ vector<TrainingInstance> v;
+ for (unsigned i = 0; i < gamma; ++i) {
+ size_t a = rng->inclusive(0, J_i.size() - 1)();
+ size_t b = rng->inclusive(0, J_i.size() - 1)();
+ if (a == b) continue;
+ double ga = J_i[a].g(scorer);
+ double gb = J_i[b].g(scorer);
+ bool positive = ga < gb;
+ if (invert_score) positive = !positive;
+ double gdiff = fabs(ga - gb);
+ if (!gdiff) continue;
+ if (rng->next() < alpha_i(gdiff)) {
+ v.push_back(TrainingInstance((J_i[a].x - J_i[b].x).erase_zeros(), positive, gdiff));
+#ifdef DEBUGGING_PRO
+ v.back().a = J_i[a].hyp;
+ v.back().b = J_i[b].hyp;
+#endif
+ }
+ }
+ vector<TrainingInstance>::iterator mid = v.begin() + xi;
+ if (xi > v.size()) mid = v.end();
+ partial_sort(v.begin(), mid, v.end(), DiffOrder());
+ copy(v.begin(), mid, back_inserter(*pv));
+#ifdef DEBUGGING_PRO
+ if (v.size() >= 5)
+ for (int i =0; i < 5; ++i) {
+ cerr << v[i].gdiff << " y=" << v[i].y << "\tA:" << TD::GetString(v[i].a) << "\n\tB: " << TD::GetString(v[i].b) << endl;
+ }
+#endif
+}
+
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
@@ -81,7 +146,15 @@ int main(int argc, char** argv) {
const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
const unsigned gamma = conf["candidate_pairs"].as<unsigned>();
const unsigned xi = conf["best_pairs"].as<unsigned>();
+ vector<string> weights_files = conf["weights"].as<vector<string> >();
+ vector<vector<double> > weights(weights_files.size());
+ for (int i = 0; i < weights.size(); ++i) {
+ Weights w;
+ w.InitFromFile(weights_files[i]);
+ w.InitVector(&weights[i]);
+ }
while(in) {
+ vector<TrainingInstance> v;
string line;
getline(in, line);
if (line.empty()) continue;
@@ -92,18 +165,27 @@ int main(int argc, char** argv) {
is >> file >> sent_id;
ReadFile rf(file);
HypergraphIO::ReadFromJSON(rf.stream(), &hg);
- KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
-
vector<HypInfo> J_i;
- 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;
- float sentscore = ds[sent_id]->ScoreCandidate(d->yield)->ComputeScore();
- // if (invert_score) sentscore *= -1.0;
- // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl;
- d->feature_values;
- sentscore;
+ int start = weights.size();
+ start -= 4;
+ if (start < 0) start = 0;
+ for (int i = start; i < weights.size(); ++i) {
+ hg.Reweight(weights[i]);
+ 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;
+ J_i.push_back(HypInfo(d->yield, d->feature_values));
+ }
+ }
+
+ Sample(gamma, xi, J_i, *ds[sent_id], ThresholdAlpha(0.05), (type == TER), &v);
+ for (unsigned i = 0; i < v.size(); ++i) {
+ const TrainingInstance& vi = v[i];
+ cout << vi.y << "\t" << vi.x << endl;
+ cout << (!vi.y) << "\t" << (vi.x * -1.0) << endl;
}
}
return 0;
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 08:15:11 +0000