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-rw-r--r--pro-train/mr_pro_map.cc351
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diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
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+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <tr1/unordered_map>
+
+#include <boost/functional/hash.hpp>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "sampler.h"
+#include "filelib.h"
+#include "stringlib.h"
+#include "weights.h"
+#include "scorer.h"
+#include "inside_outside.h"
+#include "hg_io.h"
+#include "kbest.h"
+#include "viterbi.h"
+
+// This is Figure 4 (Algorithm Sampler) from Hopkins&May (2011)
+
+using namespace std;
+namespace po = boost::program_options;
+
+struct ApproxVectorHasher {
+ static const size_t MASK = 0xFFFFFFFFull;
+ union UType {
+ double f;
+ size_t i;
+ };
+ static inline double round(const double x) {
+ UType t;
+ t.f = x;
+ size_t r = t.i & MASK;
+ if ((r << 1) > MASK)
+ t.i += MASK - r + 1;
+ else
+ t.i &= (1ull - MASK);
+ return t.f;
+ }
+ size_t operator()(const SparseVector<double>& x) const {
+ size_t h = 0x573915839;
+ for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) {
+ UType t;
+ t.f = it->second;
+ if (t.f) {
+ size_t z = (t.i >> 32);
+ boost::hash_combine(h, it->first);
+ boost::hash_combine(h, z);
+ }
+ }
+ return h;
+ }
+};
+
+struct ApproxVectorEquals {
+ bool operator()(const SparseVector<double>& a, const SparseVector<double>& b) const {
+ SparseVector<double>::const_iterator bit = b.begin();
+ for (SparseVector<double>::const_iterator ait = a.begin(); ait != a.end(); ++ait) {
+ if (bit == b.end() ||
+ ait->first != bit->first ||
+ ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second))
+ return false;
+ ++bit;
+ }
+ if (bit != b.end()) return false;
+ return true;
+ }
+};
+
+boost::shared_ptr<MT19937> rng;
+
+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)")
+ ("weights,w",po::value<string>(), "[REQD] Weights files from current iterations")
+ ("kbest_repository,K",po::value<string>()->default_value("./kbest"),"K-best list repository (directory)")
+ ("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
+ ("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")
+ ("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)")
+ ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+ ("help,h", "Help");
+ po::options_description dcmdline_options;
+ dcmdline_options.add(opts);
+ po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+ bool flag = false;
+ if (!conf->count("reference")) {
+ cerr << "Please specify one or more references using -r <REF.TXT>\n";
+ flag = true;
+ }
+ if (!conf->count("weights")) {
+ cerr << "Please specify weights using -w <WEIGHTS.TXT>\n";
+ flag = true;
+ }
+ if (flag || conf->count("help")) {
+ cerr << dcmdline_options << endl;
+ exit(1);
+ }
+}
+
+struct HypInfo {
+ HypInfo() : g_(-100.0) {}
+ 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_;
+ }
+ vector<WordID> hyp;
+ mutable double g_;
+ SparseVector<double> x;
+};
+
+struct HypInfoCompare {
+ bool operator()(const HypInfo& a, const HypInfo& b) const {
+ ApproxVectorEquals comp;
+ return (a.hyp == b.hyp && comp(a.x,b.x));
+ }
+};
+
+struct HypInfoHasher {
+ size_t operator()(const HypInfo& x) const {
+ boost::hash<vector<WordID> > hhasher;
+ ApproxVectorHasher vhasher;
+ size_t ha = hhasher(x.hyp);
+ boost::hash_combine(ha, vhasher(x.x));
+ return ha;
+ }
+};
+
+void WriteKBest(const string& file, const vector<HypInfo>& kbest) {
+ WriteFile wf(file);
+ ostream& out = *wf.stream();
+ out.precision(10);
+ for (int i = 0; i < kbest.size(); ++i) {
+ out << TD::GetString(kbest[i].hyp) << endl;
+ out << kbest[i].x << endl;
+ }
+}
+
+void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) {
+ SparseVector<double>& x = *out;
+ size_t last_start = cur;
+ size_t last_comma = string::npos;
+ while(cur <= line.size()) {
+ if (line[cur] == ' ' || cur == line.size()) {
+ if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
+ cerr << "[ERROR] " << line << endl << " position = " << cur << endl;
+ exit(1);
+ }
+ const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
+ if (cur < line.size()) line[cur] = 0;
+ const double val = strtod(&line[last_comma + 1], NULL);
+ x.set_value(fid, val);
+
+ last_comma = string::npos;
+ last_start = cur+1;
+ } else {
+ if (line[cur] == '=')
+ last_comma = cur;
+ }
+ ++cur;
+ }
+}
+
+void ReadKBest(const string& file, vector<HypInfo>* kbest) {
+ cerr << "Reading from " << file << endl;
+ ReadFile rf(file);
+ istream& in = *rf.stream();
+ string cand;
+ string feats;
+ while(getline(in, cand)) {
+ getline(in, feats);
+ assert(in);
+ kbest->push_back(HypInfo());
+ TD::ConvertSentence(cand, &kbest->back().hyp);
+ ParseSparseVector(feats, 0, &kbest->back().x);
+ }
+ cerr << " read " << kbest->size() << " hypotheses\n";
+}
+
+void Dedup(vector<HypInfo>* h) {
+ cerr << "Dedup in=" << h->size();
+ tr1::unordered_set<HypInfo, HypInfoHasher, HypInfoCompare> u;
+ while(h->size() > 0) {
+ u.insert(h->back());
+ h->pop_back();
+ }
+ tr1::unordered_set<HypInfo, HypInfoHasher, HypInfoCompare>::iterator it = u.begin();
+ while (it != u.end()) {
+ h->push_back(*it);
+ it = u.erase(it);
+ }
+ cerr << " out=" << h->size() << endl;
+}
+
+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;
+#undef DEBUGGING_PRO
+#ifdef DEBUGGING_PRO
+ vector<WordID> a;
+ vector<WordID> b;
+#endif
+ bool y;
+ double gdiff;
+};
+#ifdef DEBUGGING_PRO
+ostream& operator<<(ostream& os, const TrainingInstance& d) {
+ return os << d.gdiff << " y=" << d.y << "\tA:" << TD::GetString(d.a) << "\n\tB: " << TD::GetString(d.b) << "\n\tX: " << d.x;
+}
+#endif
+
+struct DiffOrder {
+ bool operator()(const TrainingInstance& a, const TrainingInstance& b) const {
+ return a.gdiff > b.gdiff;
+ }
+};
+
+void Sample(const unsigned gamma, const unsigned xi, const vector<HypInfo>& J_i, const SentenceScorer& scorer, const bool invert_score, vector<TrainingInstance>* pv) {
+ vector<TrainingInstance> v1, v2;
+ double avg_diff = 0;
+ for (unsigned i = 0; i < gamma; ++i) {
+ const size_t a = rng->inclusive(0, J_i.size() - 1)();
+ const 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 = gb < ga;
+ if (invert_score) positive = !positive;
+ const double gdiff = fabs(ga - gb);
+ if (!gdiff) continue;
+ avg_diff += gdiff;
+ SparseVector<double> xdiff = (J_i[a].x - J_i[b].x).erase_zeros();
+ if (xdiff.empty()) {
+ cerr << "Empty diff:\n " << TD::GetString(J_i[a].hyp) << endl << "x=" << J_i[a].x << endl;
+ cerr << " " << TD::GetString(J_i[b].hyp) << endl << "x=" << J_i[b].x << endl;
+ continue;
+ }
+ v1.push_back(TrainingInstance(xdiff, positive, gdiff));
+#ifdef DEBUGGING_PRO
+ v1.back().a = J_i[a].hyp;
+ v1.back().b = J_i[b].hyp;
+ cerr << "N: " << v1.back() << endl;
+#endif
+ }
+ avg_diff /= v1.size();
+
+ for (unsigned i = 0; i < v1.size(); ++i) {
+ double p = 1.0 / (1.0 + exp(-avg_diff - v1[i].gdiff));
+ // cerr << "avg_diff=" << avg_diff << " gdiff=" << v1[i].gdiff << " p=" << p << endl;
+ if (rng->next() < p) v2.push_back(v1[i]);
+ }
+ vector<TrainingInstance>::iterator mid = v2.begin() + xi;
+ if (xi > v2.size()) mid = v2.end();
+ partial_sort(v2.begin(), mid, v2.end(), DiffOrder());
+ copy(v2.begin(), mid, back_inserter(*pv));
+#ifdef DEBUGGING_PRO
+ if (v2.size() >= 5) {
+ for (int i =0; i < (mid - v2.begin()); ++i) {
+ cerr << v2[i] << endl;
+ }
+ cerr << pv->back() << endl;
+ }
+#endif
+}
+
+int main(int argc, char** argv) {
+ po::variables_map conf;
+ InitCommandLine(argc, argv, &conf);
+ if (conf.count("random_seed"))
+ rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+ else
+ rng.reset(new MT19937);
+ const string loss_function = conf["loss_function"].as<string>();
+
+ ScoreType type = ScoreTypeFromString(loss_function);
+ DocScorer ds(type, conf["reference"].as<vector<string> >(), conf["source"].as<string>());
+ cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function << endl;
+ Hypergraph hg;
+ string last_file;
+ ReadFile in_read(conf["input"].as<string>());
+ istream &in=*in_read.stream();
+ 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>();
+ string weightsf = conf["weights"].as<string>();
+ vector<double> weights;
+ {
+ Weights w;
+ w.InitFromFile(weightsf);
+ w.InitVector(&weights);
+ }
+ string kbest_repo = conf["kbest_repository"].as<string>();
+ MkDirP(kbest_repo);
+ while(in) {
+ vector<TrainingInstance> v;
+ string line;
+ getline(in, line);
+ if (line.empty()) continue;
+ istringstream is(line);
+ int sent_id;
+ string file;
+ // path-to-file (JSON) sent_id
+ is >> file >> sent_id;
+ ReadFile rf(file);
+ ostringstream os;
+ vector<HypInfo> J_i;
+ os << kbest_repo << "/kbest." << sent_id << ".txt.gz";
+ const string kbest_file = os.str();
+ if (FileExists(kbest_file))
+ ReadKBest(kbest_file, &J_i);
+ 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;
+ J_i.push_back(HypInfo(d->yield, d->feature_values));
+ }
+ Dedup(&J_i);
+ WriteKBest(kbest_file, J_i);
+
+ Sample(gamma, xi, J_i, *ds[sent_id], (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;
+}
+
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