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// -*- mode: c++; tab-width: 2; indent-tabs-mode: nil -*-
#pragma once
#include <algorithm>
#include <boost/random.hpp>
#include <boost/thread.hpp>
#include <boost/thread/locks.hpp>
#include <boost/intrusive_ptr.hpp>
#include <boost/math/distributions/binomial.hpp>
#include "ug_bitext.h"
#include "ug_bitext_pstats.h"
#include "ug_sampling_bias.h"
#include "ug_tsa_array_entry.h"
#include "ug_bitext_phrase_extraction_record.h"
#include "moses/TranslationModel/UG/generic/threading/ug_ref_counter.h"
#include "moses/TranslationModel/UG/generic/threading/ug_thread_safe_counter.h"
#include "moses/TranslationModel/UG/generic/sorting/NBestList.h"
namespace sapt
{
enum
sampling_method
{
full_coverage,
random_sampling,
ranked_sampling,
ranked_sampling2
};
typedef ttrack::Position TokenPosition;
class CandidateSorter
{
SamplingBias const& score;
public:
CandidateSorter(SamplingBias const& s) : score(s) {}
bool operator()(TokenPosition const& a, TokenPosition const& b) const
{ return score[a.sid] > score[b.sid]; }
};
template<typename Token>
class
BitextSampler : public Moses::reference_counter
{
typedef Bitext<Token> bitext;
typedef TSA<Token> tsa;
typedef SamplingBias bias_t;
typedef typename Bitext<Token>::iter tsa_iter;
mutable boost::condition_variable m_ready;
mutable boost::mutex m_lock;
// const members
// SPTR<bitext const> const m_bitext; // keep bitext alive while I am
// should be an
SPTR<bitext const> const m_bitext; // keep bitext alive as long as I am
size_t const m_plen; // length of lookup phrase
bool const m_fwd; // forward or backward direction?
SPTR<tsa const> const m_root; // root of suffix array
char const* m_next; // current position
char const* m_stop; // end of search range
sampling_method const m_method; // look at all/random/ranked samples
SPTR<bias_t const> const m_bias; // bias over candidates
size_t const m_samples; // how many samples at most
size_t const m_min_samples;
// non-const members
SPTR<pstats> m_stats; // destination for phrase stats
size_t m_ctr; // number of samples considered
float m_total_bias; // for random sampling with bias
bool m_finished;
size_t m_num_occurrences; // estimated number of phrase occurrences in corpus
boost::taus88 m_rnd; // every job has its own pseudo random generator
double m_bias_total;
bool m_track_sids; // track sentence ids in stats?
size_t consider_sample(TokenPosition const& p);
size_t perform_random_sampling();
size_t perform_full_phrase_extraction();
int check_sample_distribution(uint64_t const& sid, uint64_t const& offset);
bool flip_coin(id_type const& sid, ushort const& offset, SamplingBias const* bias);
public:
BitextSampler(BitextSampler const& other);
// BitextSampler const& operator=(BitextSampler const& other);
BitextSampler(SPTR<bitext const> const& bitext,
typename bitext::iter const& phrase,
SPTR<SamplingBias const> const& bias,
size_t const min_samples,
size_t const max_samples,
sampling_method const method,
bool const track_sids);
~BitextSampler();
SPTR<pstats> stats();
bool done() const;
#ifdef MMT
#include "mmt_bitext_sampler-inc.h"
#else
bool operator()(); // run sampling
#endif
};
template<typename Token>
int
BitextSampler<Token>::
check_sample_distribution(uint64_t const& sid, uint64_t const& offset)
{ // ensure that the sampled distribution approximately matches the bias
// @return 0: SKIP this occurrence
// @return 1: consider this occurrence for sampling
// @return 2: include this occurrence in the sample by all means
typedef boost::math::binomial_distribution<> binomial;
// std::ostream* log = m_bias->loglevel > 1 ? m_bias->log : NULL;
std::ostream* log = NULL;
if (!m_bias) return 1;
float p = (*m_bias)[sid];
id_type docid = m_bias->GetClass(sid);
pstats::indoc_map_t::const_iterator m = m_stats->indoc.find(docid);
uint32_t k = m != m_stats->indoc.end() ? m->second : 0 ;
// always consider candidates from dominating documents and
// from documents that have not been considered at all yet
bool ret = (p > .5 || k == 0);
if (ret && !log) return 1;
uint32_t N = m_stats->good; // number of trials
float d = cdf(complement(binomial(N, p), k));
// d: probability that samples contains k or more instances from doc #docid
ret = ret || d >= .05;
#if 0
if (log)
{
Token const* t = m_root->getCorpus()->sntStart(sid)+offset;
Token const* x = t - min(offset,uint64_t(3));
Token const* e = t + 4;
if (e > m_root->getCorpus()->sntEnd(sid))
e = m_root->getCorpus()->sntEnd(sid);
*log << docid << ":" << sid << " " << size_t(k) << "/" << N
<< " @" << p << " => " << d << " [";
pstats::indoc_map_t::const_iterator m;
for (m = m_stats->indoc.begin(); m != m_stats->indoc.end(); ++m)
{
if (m != m_stats->indoc.begin()) *log << " ";
*log << m->first << ":" << m->second;
}
*log << "] ";
for (; x < e; ++x) *log << (*m_bitext->V1)[x->id()] << " ";
if (!ret) *log << "SKIP";
else if (p < .5 && d > .9) *log << "FORCE";
*log << std::endl;
}
#endif
return (ret ? (p < .5 && d > .9) ? 2 : 1 : 0);
}
template<typename Token>
bool
BitextSampler<Token>::
flip_coin(id_type const& sid, ushort const& offset, bias_t const* bias)
{
int no_maybe_yes = bias ? check_sample_distribution(sid, offset) : 1;
if (no_maybe_yes == 0) return false; // no
if (no_maybe_yes > 1) return true; // yes
// ... maybe: flip a coin
size_t options_chosen = m_stats->good;
size_t options_total = std::max(m_stats->raw_cnt, m_ctr);
size_t options_left = (options_total - m_ctr);
size_t random_number = options_left * (m_rnd()/(m_rnd.max()+1.));
size_t threshold;
if (bias && m_bias_total > 0) // we have a bias and there are candidates with non-zero prob
threshold = ((*bias)[sid]/m_bias_total * options_total * m_samples);
else // no bias, or all have prob 0 (can happen with a very opinionated bias)
threshold = m_samples;
return random_number + options_chosen < threshold;
}
template<typename Token>
BitextSampler<Token>::
BitextSampler(SPTR<Bitext<Token> const> const& bitext,
typename bitext::iter const& phrase,
SPTR<SamplingBias const> const& bias, size_t const min_samples, size_t const max_samples,
sampling_method const method, bool const track_sids)
: m_bitext(bitext)
, m_plen(phrase.size())
, m_fwd(phrase.root == bitext->I1.get())
, m_root(m_fwd ? bitext->I1 : bitext->I2)
, m_next(phrase.lower_bound(-1))
, m_stop(phrase.upper_bound(-1))
, m_method(method)
, m_bias(bias)
, m_samples(max_samples)
, m_min_samples(min_samples)
, m_ctr(0)
, m_total_bias(0)
, m_finished(false)
, m_num_occurrences(phrase.ca())
, m_rnd(0)
, m_track_sids(track_sids)
{
m_stats.reset(new pstats(m_track_sids));
m_stats->raw_cnt = phrase.ca();
m_stats->register_worker();
}
template<typename Token>
BitextSampler<Token>::
BitextSampler(BitextSampler const& other)
: m_bitext(other.m_bitext)
, m_plen(other.m_plen)
, m_fwd(other.m_fwd)
, m_root(other.m_root)
, m_next(other.m_next)
, m_stop(other.m_stop)
, m_method(other.m_method)
, m_bias(other.m_bias)
, m_samples(other.m_samples)
, m_min_samples(other.m_min_samples)
, m_num_occurrences(other.m_num_occurrences)
, m_rnd(0)
{
// lock both instances
boost::unique_lock<boost::mutex> mylock(m_lock);
boost::unique_lock<boost::mutex> yrlock(other.m_lock);
// actually, BitextSamplers should only copied on job submission
m_stats = other.m_stats;
m_stats->register_worker();
m_ctr = other.m_ctr;
m_total_bias = other.m_total_bias;
m_finished = other.m_finished;
}
// Uniform sampling
template<typename Token>
size_t
BitextSampler<Token>::
perform_full_phrase_extraction()
{
if (m_next == m_stop) return m_ctr;
for (sapt::tsa::ArrayEntry I(m_next); I.next < m_stop; ++m_ctr)
{
++m_ctr;
m_root->readEntry(I.next, I);
consider_sample(I);
}
return m_ctr;
}
// Uniform sampling
template<typename Token>
size_t
BitextSampler<Token>::
perform_random_sampling()
{
if (m_next == m_stop) return m_ctr;
m_bias_total = 0;
sapt::tsa::ArrayEntry I(m_next);
if (m_bias)
{
m_stats->raw_cnt = 0;
while (I.next < m_stop)
{
m_root->readEntry(I.next,I);
++m_stats->raw_cnt;
m_bias_total += (*m_bias)[I.sid];
}
I.next = m_next;
}
while (m_stats->good < m_samples && I.next < m_stop)
{
++m_ctr;
m_root->readEntry(I.next,I);
if (!flip_coin(I.sid, I.offset, m_bias.get())) continue;
consider_sample(I);
}
return m_ctr;
}
template<typename Token>
size_t
BitextSampler<Token>::
consider_sample(TokenPosition const& p)
{
std::vector<unsigned char> aln;
bitvector full_aln(100*100);
PhraseExtractionRecord
rec(p.sid, p.offset, p.offset + m_plen, !m_fwd, &aln, &full_aln);
int docid = m_bias ? m_bias->GetClass(p.sid) : m_bitext->sid2did(p.sid);
if (!m_bitext->find_trg_phr_bounds(rec))
{ // no good, probably because phrase is not coherent
m_stats->count_sample(docid, 0, rec.po_fwd, rec.po_bwd);
return 0;
}
// all good: register this sample as valid
size_t num_pairs = (rec.s2 - rec.s1 + 1) * (rec.e2 - rec.e1 + 1);
m_stats->count_sample(docid, num_pairs, rec.po_fwd, rec.po_bwd);
float sample_weight = 1./num_pairs;
Token const* o = (m_fwd ? m_bitext->T2 : m_bitext->T1)->sntStart(rec.sid);
// adjust offsets in phrase-internal aligment
for (size_t k = 1; k < aln.size(); k += 2)
aln[k] += rec.s2 - rec.s1;
std::vector<uint64_t> seen; seen.reserve(10);
// It is possible that the phrase extraction extracts the same
// phrase twice, e.g., when word a co-occurs with sequence b b b but
// is aligned only to the middle word. We can only count each phrase
// pair once per source phrase occurrence, or else run the risk of
// having more joint counts than marginal counts.
size_t max_evidence = 0;
for (size_t s = rec.s1; s <= rec.s2; ++s)
{
TSA<Token> const& I = m_fwd ? *m_bitext->I2 : *m_bitext->I1;
SPTR<tsa_iter> b = I.find(o + s, rec.e1 - s);
UTIL_THROW_IF2(!b || b->size() < rec.e1 - s, "target phrase not found");
for (size_t i = rec.e1; i <= rec.e2; ++i)
{
uint64_t tpid = b->getPid();
if (find(seen.begin(), seen.end(), tpid) != seen.end())
continue; // don't over-count
seen.push_back(tpid);
size_t raw2 = b->approxOccurrenceCount();
size_t evid = m_stats->add(tpid, sample_weight,
m_bias ? (*m_bias)[p.sid] : 1,
aln, raw2, rec.po_fwd, rec.po_bwd, docid,
p.sid);
max_evidence = std::max(max_evidence, evid);
bool ok = (i == rec.e2) || b->extend(o[i].id());
UTIL_THROW_IF2(!ok, "Could not extend target phrase.");
}
if (s < rec.s2) // shift phrase-internal alignments
for (size_t k = 1; k < aln.size(); k += 2)
--aln[k];
}
return max_evidence;
}
#ifndef MMT
template<typename Token>
bool
BitextSampler<Token>::
operator()()
{
if (m_finished) return true;
boost::unique_lock<boost::mutex> lock(m_lock);
if (m_method == full_coverage)
perform_full_phrase_extraction(); // consider all occurrences
else if (m_method == random_sampling)
perform_random_sampling();
else UTIL_THROW2("Unsupported sampling method.");
m_finished = true;
m_ready.notify_all();
return true;
}
#endif
template<typename Token>
bool
BitextSampler<Token>::
done() const
{
return m_next == m_stop;
}
template<typename Token>
SPTR<pstats>
BitextSampler<Token>::
stats()
{
// if (m_ctr == 0) (*this)();
// boost::unique_lock<boost::mutex> lock(m_lock);
// while (!m_finished)
// m_ready.wait(lock);
return m_stats;
}
template<typename Token>
BitextSampler<Token>::
~BitextSampler()
{
m_stats->release();
}
} // end of namespace sapt
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