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-rw-r--r--gi/pf/learn_cfg.cc428
1 files changed, 0 insertions, 428 deletions
diff --git a/gi/pf/learn_cfg.cc b/gi/pf/learn_cfg.cc
deleted file mode 100644
index 1d5126e4..00000000
--- a/gi/pf/learn_cfg.cc
+++ /dev/null
@@ -1,428 +0,0 @@
-#include <iostream>
-#include <tr1/memory>
-#include <queue>
-
-#include <boost/functional.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "inside_outside.h"
-#include "hg.h"
-#include "bottom_up_parser.h"
-#include "fdict.h"
-#include "grammar.h"
-#include "m.h"
-#include "trule.h"
-#include "tdict.h"
-#include "filelib.h"
-#include "dict.h"
-#include "sampler.h"
-#include "ccrp.h"
-#include "ccrp_onetable.h"
-
-using namespace std;
-using namespace tr1;
-namespace po = boost::program_options;
-
-boost::shared_ptr<MT19937> prng;
-vector<int> nt_vocab;
-vector<int> nt_id_to_index;
-static unsigned kMAX_RULE_SIZE = 0;
-static unsigned kMAX_ARITY = 0;
-static bool kALLOW_MIXED = true; // allow rules with mixed terminals and NTs
-static bool kHIERARCHICAL_PRIOR = false;
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
- po::options_description opts("Configuration options");
- opts.add_options()
- ("samples,s",po::value<unsigned>()->default_value(1000),"Number of samples")
- ("input,i",po::value<string>(),"Read parallel data from")
- ("max_rule_size,m", po::value<unsigned>()->default_value(0), "Maximum rule size (0 for unlimited)")
- ("max_arity,a", po::value<unsigned>()->default_value(0), "Maximum number of nonterminals in a rule (0 for unlimited)")
- ("no_mixed_rules,M", "Do not mix terminals and nonterminals in a rule RHS")
- ("nonterminals,n", po::value<unsigned>()->default_value(1), "Size of nonterminal vocabulary")
- ("hierarchical_prior,h", "Use hierarchical prior")
- ("random_seed,S",po::value<uint32_t>(), "Random seed");
- po::options_description clo("Command line options");
- clo.add_options()
- ("config", po::value<string>(), "Configuration file")
- ("help", "Print this help message and exit");
- po::options_description dconfig_options, dcmdline_options;
- dconfig_options.add(opts);
- dcmdline_options.add(opts).add(clo);
-
- po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
- if (conf->count("config")) {
- ifstream config((*conf)["config"].as<string>().c_str());
- po::store(po::parse_config_file(config, dconfig_options), *conf);
- }
- po::notify(*conf);
-
- if (conf->count("help") || (conf->count("input") == 0)) {
- cerr << dcmdline_options << endl;
- exit(1);
- }
-}
-
-unsigned ReadCorpus(const string& filename,
- vector<vector<WordID> >* e,
- set<WordID>* vocab_e) {
- e->clear();
- vocab_e->clear();
- istream* in;
- if (filename == "-")
- in = &cin;
- else
- in = new ifstream(filename.c_str());
- assert(*in);
- string line;
- unsigned toks = 0;
- while(*in) {
- getline(*in, line);
- if (line.empty() && !*in) break;
- e->push_back(vector<int>());
- vector<int>& le = e->back();
- TD::ConvertSentence(line, &le);
- for (unsigned i = 0; i < le.size(); ++i)
- vocab_e->insert(le[i]);
- toks += le.size();
- }
- if (in != &cin) delete in;
- return toks;
-}
-
-struct Grid {
- // a b c d e
- // 0 - 0 - -
- vector<int> grid;
-};
-
-struct BaseRuleModel {
- explicit BaseRuleModel(unsigned term_size,
- unsigned nonterm_size = 1) :
- unif_term(1.0 / term_size),
- unif_nonterm(1.0 / nonterm_size) {}
- prob_t operator()(const TRule& r) const {
- prob_t p; p.logeq(Md::log_poisson(1.0, r.f_.size()));
- const prob_t term_prob((2.0 + 0.01*r.f_.size()) / (r.f_.size() + 2));
- const prob_t nonterm_prob(1.0 - term_prob.as_float());
- for (unsigned i = 0; i < r.f_.size(); ++i) {
- if (r.f_[i] <= 0) { // nonterminal
- if (kALLOW_MIXED) p *= nonterm_prob;
- p *= unif_nonterm;
- } else { // terminal
- if (kALLOW_MIXED) p *= term_prob;
- p *= unif_term;
- }
- }
- return p;
- }
- const prob_t unif_term, unif_nonterm;
-};
-
-struct HieroLMModel {
- explicit HieroLMModel(unsigned vocab_size, unsigned num_nts = 1) :
- base(vocab_size, num_nts),
- q0(1,1,1,1),
- nts(num_nts, CCRP<TRule>(1,1,1,1)) {}
-
- prob_t Prob(const TRule& r) const {
- return nts[nt_id_to_index[-r.lhs_]].prob(r, p0(r));
- }
-
- inline prob_t p0(const TRule& r) const {
- if (kHIERARCHICAL_PRIOR)
- return q0.prob(r, base(r));
- else
- return base(r);
- }
-
- int Increment(const TRule& r, MT19937* rng) {
- const int delta = nts[nt_id_to_index[-r.lhs_]].increment(r, p0(r), rng);
- if (kHIERARCHICAL_PRIOR && delta)
- q0.increment(r, base(r), rng);
- return delta;
- // return x.increment(r);
- }
-
- int Decrement(const TRule& r, MT19937* rng) {
- const int delta = nts[nt_id_to_index[-r.lhs_]].decrement(r, rng);
- if (kHIERARCHICAL_PRIOR && delta)
- q0.decrement(r, rng);
- return delta;
- //return x.decrement(r);
- }
-
- prob_t Likelihood() const {
- prob_t p = prob_t::One();
- for (unsigned i = 0; i < nts.size(); ++i) {
- prob_t q; q.logeq(nts[i].log_crp_prob());
- p *= q;
- for (CCRP<TRule>::const_iterator it = nts[i].begin(); it != nts[i].end(); ++it) {
- prob_t tp = p0(it->first);
- tp.poweq(it->second.num_tables());
- p *= tp;
- }
- }
- if (kHIERARCHICAL_PRIOR) {
- prob_t q; q.logeq(q0.log_crp_prob());
- p *= q;
- for (CCRP<TRule>::const_iterator it = q0.begin(); it != q0.end(); ++it) {
- prob_t tp = base(it->first);
- tp.poweq(it->second.num_tables());
- p *= tp;
- }
- }
- //for (CCRP_OneTable<TRule>::const_iterator it = x.begin(); it != x.end(); ++it)
- // p *= base(it->first);
- return p;
- }
-
- void ResampleHyperparameters(MT19937* rng) {
- for (unsigned i = 0; i < nts.size(); ++i)
- nts[i].resample_hyperparameters(rng);
- if (kHIERARCHICAL_PRIOR) {
- q0.resample_hyperparameters(rng);
- cerr << "[base d=" << q0.discount() << ", s=" << q0.strength() << "]";
- }
- cerr << " d=" << nts[0].discount() << ", s=" << nts[0].strength() << endl;
- }
-
- const BaseRuleModel base;
- CCRP<TRule> q0;
- vector<CCRP<TRule> > nts;
- //CCRP_OneTable<TRule> x;
-};
-
-vector<GrammarIter* > tofreelist;
-
-HieroLMModel* plm;
-
-struct NPGrammarIter : public GrammarIter, public RuleBin {
- NPGrammarIter() : arity() { tofreelist.push_back(this); }
- NPGrammarIter(const TRulePtr& inr, const int a, int symbol) : arity(a) {
- if (inr) {
- r.reset(new TRule(*inr));
- } else {
- r.reset(new TRule);
- }
- TRule& rr = *r;
- rr.lhs_ = nt_vocab[0];
- rr.f_.push_back(symbol);
- rr.e_.push_back(symbol < 0 ? (1-int(arity)) : symbol);
- tofreelist.push_back(this);
- }
- inline static unsigned NextArity(int cur_a, int symbol) {
- return cur_a + (symbol <= 0 ? 1 : 0);
- }
- virtual int GetNumRules() const {
- if (r) return nt_vocab.size(); else return 0;
- }
- virtual TRulePtr GetIthRule(int i) const {
- if (i == 0) return r;
- TRulePtr nr(new TRule(*r));
- nr->lhs_ = nt_vocab[i];
- return nr;
- }
- virtual int Arity() const {
- return arity;
- }
- virtual const RuleBin* GetRules() const {
- if (!r) return NULL; else return this;
- }
- virtual const GrammarIter* Extend(int symbol) const {
- const int next_arity = NextArity(arity, symbol);
- if (kMAX_ARITY && next_arity > kMAX_ARITY)
- return NULL;
- if (!kALLOW_MIXED && r) {
- bool t1 = r->f_.front() <= 0;
- bool t2 = symbol <= 0;
- if (t1 != t2) return NULL;
- }
- if (!kMAX_RULE_SIZE || !r || (r->f_.size() < kMAX_RULE_SIZE))
- return new NPGrammarIter(r, next_arity, symbol);
- else
- return NULL;
- }
- const unsigned char arity;
- TRulePtr r;
-};
-
-struct NPGrammar : public Grammar {
- virtual const GrammarIter* GetRoot() const {
- return new NPGrammarIter;
- }
-};
-
-prob_t TotalProb(const Hypergraph& hg) {
- return Inside<prob_t, EdgeProb>(hg);
-}
-
-void SampleDerivation(const Hypergraph& hg, MT19937* rng, vector<unsigned>* sampled_deriv) {
- vector<prob_t> node_probs;
- Inside<prob_t, EdgeProb>(hg, &node_probs);
- queue<unsigned> q;
- q.push(hg.nodes_.size() - 2);
- while(!q.empty()) {
- unsigned cur_node_id = q.front();
-// cerr << "NODE=" << cur_node_id << endl;
- q.pop();
- const Hypergraph::Node& node = hg.nodes_[cur_node_id];
- const unsigned num_in_edges = node.in_edges_.size();
- unsigned sampled_edge = 0;
- if (num_in_edges == 1) {
- sampled_edge = node.in_edges_[0];
- } else {
- //prob_t z;
- assert(num_in_edges > 1);
- SampleSet<prob_t> ss;
- for (unsigned j = 0; j < num_in_edges; ++j) {
- const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
- prob_t p = edge.edge_prob_;
- for (unsigned k = 0; k < edge.tail_nodes_.size(); ++k)
- p *= node_probs[edge.tail_nodes_[k]];
- ss.add(p);
-// cerr << log(ss[j]) << " ||| " << edge.rule_->AsString() << endl;
- //z += p;
- }
-// for (unsigned j = 0; j < num_in_edges; ++j) {
-// const Hypergraph::Edge& edge = hg.edges_[node.in_edges_[j]];
-// cerr << exp(log(ss[j] / z)) << " ||| " << edge.rule_->AsString() << endl;
-// }
-// cerr << " --- \n";
- sampled_edge = node.in_edges_[rng->SelectSample(ss)];
- }
- sampled_deriv->push_back(sampled_edge);
- const Hypergraph::Edge& edge = hg.edges_[sampled_edge];
- for (unsigned j = 0; j < edge.tail_nodes_.size(); ++j) {
- q.push(edge.tail_nodes_[j]);
- }
- }
- for (unsigned i = 0; i < sampled_deriv->size(); ++i) {
- cerr << *hg.edges_[(*sampled_deriv)[i]].rule_ << endl;
- }
-}
-
-void IncrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, HieroLMModel* plm, MT19937* rng) {
- for (unsigned i = 0; i < d.size(); ++i)
- plm->Increment(*hg.edges_[d[i]].rule_, rng);
-}
-
-void DecrementDerivation(const Hypergraph& hg, const vector<unsigned>& d, HieroLMModel* plm, MT19937* rng) {
- for (unsigned i = 0; i < d.size(); ++i)
- plm->Decrement(*hg.edges_[d[i]].rule_, rng);
-}
-
-int main(int argc, char** argv) {
- po::variables_map conf;
-
- InitCommandLine(argc, argv, &conf);
- nt_vocab.resize(conf["nonterminals"].as<unsigned>());
- assert(nt_vocab.size() > 0);
- assert(nt_vocab.size() < 26);
- {
- string nt = "X";
- for (unsigned i = 0; i < nt_vocab.size(); ++i) {
- if (nt_vocab.size() > 1) nt[0] = ('A' + i);
- int pid = TD::Convert(nt);
- nt_vocab[i] = -pid;
- if (pid >= nt_id_to_index.size()) {
- nt_id_to_index.resize(pid + 1, -1);
- }
- nt_id_to_index[pid] = i;
- }
- }
- vector<GrammarPtr> grammars;
- grammars.push_back(GrammarPtr(new NPGrammar));
-
- const unsigned samples = conf["samples"].as<unsigned>();
- kMAX_RULE_SIZE = conf["max_rule_size"].as<unsigned>();
- if (kMAX_RULE_SIZE == 1) {
- cerr << "Invalid maximum rule size: must be 0 or >1\n";
- return 1;
- }
- kMAX_ARITY = conf["max_arity"].as<unsigned>();
- if (kMAX_ARITY == 1) {
- cerr << "Invalid maximum arity: must be 0 or >1\n";
- return 1;
- }
- kALLOW_MIXED = !conf.count("no_mixed_rules");
-
- kHIERARCHICAL_PRIOR = conf.count("hierarchical_prior");
-
- if (conf.count("random_seed"))
- prng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
- else
- prng.reset(new MT19937);
- MT19937& rng = *prng;
- vector<vector<WordID> > corpuse;
- set<WordID> vocabe;
- cerr << "Reading corpus...\n";
- const unsigned toks = ReadCorpus(conf["input"].as<string>(), &corpuse, &vocabe);
- cerr << "E-corpus size: " << corpuse.size() << " sentences\t (" << vocabe.size() << " word types)\n";
- HieroLMModel lm(vocabe.size(), nt_vocab.size());
-
- plm = &lm;
- ExhaustiveBottomUpParser parser(TD::Convert(-nt_vocab[0]), grammars);
-
- Hypergraph hg;
- const int kGoal = -TD::Convert("Goal");
- const int kLP = FD::Convert("LogProb");
- SparseVector<double> v; v.set_value(kLP, 1.0);
- vector<vector<unsigned> > derivs(corpuse.size());
- vector<Lattice> cl(corpuse.size());
- for (int ci = 0; ci < corpuse.size(); ++ci) {
- vector<int>& src = corpuse[ci];
- Lattice& lat = cl[ci];
- lat.resize(src.size());
- for (unsigned i = 0; i < src.size(); ++i)
- lat[i].push_back(LatticeArc(src[i], 0.0, 1));
- }
- for (int SS=0; SS < samples; ++SS) {
- const bool is_last = ((samples - 1) == SS);
- prob_t dlh = prob_t::One();
- for (int ci = 0; ci < corpuse.size(); ++ci) {
- const vector<int>& src = corpuse[ci];
- const Lattice& lat = cl[ci];
- cerr << TD::GetString(src) << endl;
- hg.clear();
- parser.Parse(lat, &hg); // exhaustive parse
- vector<unsigned>& d = derivs[ci];
- if (!is_last) DecrementDerivation(hg, d, &lm, &rng);
- for (unsigned i = 0; i < hg.edges_.size(); ++i) {
- TRule& r = *hg.edges_[i].rule_;
- if (r.lhs_ == kGoal)
- hg.edges_[i].edge_prob_ = prob_t::One();
- else
- hg.edges_[i].edge_prob_ = lm.Prob(r);
- }
- if (!is_last) {
- d.clear();
- SampleDerivation(hg, &rng, &d);
- IncrementDerivation(hg, derivs[ci], &lm, &rng);
- } else {
- prob_t p = TotalProb(hg);
- dlh *= p;
- cerr << " p(sentence) = " << log(p) << "\t" << log(dlh) << endl;
- }
- if (tofreelist.size() > 200000) {
- cerr << "Freeing ... ";
- for (unsigned i = 0; i < tofreelist.size(); ++i)
- delete tofreelist[i];
- tofreelist.clear();
- cerr << "Freed.\n";
- }
- }
- double llh = log(lm.Likelihood());
- cerr << "LLH=" << llh << "\tENTROPY=" << (-llh / log(2) / toks) << "\tPPL=" << pow(2, -llh / log(2) / toks) << endl;
- if (SS % 10 == 9) lm.ResampleHyperparameters(&rng);
- if (is_last) {
- double z = log(dlh);
- cerr << "TOTAL_PROB=" << z << "\tENTROPY=" << (-z / log(2) / toks) << "\tPPL=" << pow(2, -z / log(2) / toks) << endl;
- }
- }
- for (unsigned i = 0; i < nt_vocab.size(); ++i)
- cerr << lm.nts[i] << endl;
- return 0;
-}
-
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