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#include "tromble_loss.h"
#include "fast_lexical_cast.hpp"
#include <boost/algorithm/string/predicate.hpp>
#include <boost/circular_buffer.hpp>
#include <boost/functional/hash.hpp>
#include <boost/range/iterator_range.hpp>
#include <boost/tokenizer.hpp>
#include <boost/unordered_map.hpp>
#include <cmath>
#include <fstream>
#include <vector>
#include "sentence_metadata.h"
#include "trule.h"
#include "tdict.h"
using namespace std;
namespace {
typedef unsigned char GramCount;
struct RefCounts {
GramCount max;
std::vector<GramCount> refs;
size_t length;
};
typedef boost::unordered_map<std::vector<WordID>, size_t, boost::hash<std::vector<WordID> > > NGramMap;
// Take all the n-grams in the references and stuff them into ngrams.
void MakeNGramMapFromReferences(const vector<vector<WordID> > &references,
int n,
vector<RefCounts> *counts,
NGramMap *ngrams) {
ngrams->clear();
std::pair<vector<WordID>, size_t> insert_me;
vector<WordID> &ngram = insert_me.first;
ngram.reserve(n);
size_t &id = insert_me.second;
id = 0;
for (int refi = 0; refi < references.size(); ++refi) {
const vector<WordID>& ref = references[refi];
const int s = ref.size();
for (int j=0; j<s; ++j) {
const int remaining = s-j;
const int k = (n < remaining ? n : remaining);
ngram.clear();
for (unsigned int i = 0; i < k; ++i) {
ngram.push_back(ref[j + i]);
std::pair<NGramMap::iterator, bool> ret(ngrams->insert(insert_me));
if (ret.second) {
counts->resize(id + 1);
RefCounts &ref_counts = counts->back();
ref_counts.max = 1;
ref_counts.refs.resize(references.size());
ref_counts.refs[refi] = 1;
ref_counts.length = ngram.size();
++id;
} else {
RefCounts &ref_counts = (*counts)[ret.first->second];
ref_counts.max = std::max(ref_counts.max, ++ref_counts.refs[refi]);
}
}
}
}
}
struct MutableState {
MutableState(void *from, size_t n) : length(reinterpret_cast<size_t*>(from)), left(reinterpret_cast<WordID *>(length + 1)), right(left + n - 1), counts(reinterpret_cast<GramCount *>(right + n - 1)) {}
size_t *length;
WordID *left, *right;
GramCount *counts;
static size_t Size(size_t n, size_t bound_ngram_id) { return sizeof(size_t) + (n - 1) * 2 * sizeof(WordID) + bound_ngram_id * sizeof(GramCount); }
};
struct ConstState {
ConstState(const void *from, size_t n) : length(reinterpret_cast<const size_t*>(from)), left(reinterpret_cast<const WordID *>(length + 1)), right(left + n - 1), counts(reinterpret_cast<const GramCount *>(right + n - 1)) {}
const size_t *length;
const WordID *left, *right;
const GramCount *counts;
static size_t Size(size_t n, size_t bound_ngram_id) { return sizeof(size_t) + (n - 1) * 2 * sizeof(WordID) + bound_ngram_id * sizeof(GramCount); }
};
template <class T> struct CompatibleHashRange : public std::unary_function<const boost::iterator_range<T> &, size_t> {
size_t operator()(const boost::iterator_range<T> &range) const {
return boost::hash_range(range.begin(), range.end());
}
};
template <class T> struct CompatibleEqualsRange : public std::binary_function<const boost::iterator_range<T> &, const std::vector<WordID> &, size_t> {
size_t operator()(const boost::iterator_range<T> &range, const std::vector<WordID> &vec) const {
return boost::algorithm::equals(range, vec);
}
size_t operator()(const std::vector<WordID> &vec, const boost::iterator_range<T> &range) const {
return boost::algorithm::equals(range, vec);
}
};
void AddWord(const boost::circular_buffer<WordID> &segment, size_t min_length, const NGramMap &ref_grams, GramCount *counters) {
typedef boost::circular_buffer<WordID>::const_iterator BufferIt;
typedef boost::iterator_range<BufferIt> SegmentRange;
if (segment.size() < min_length) return;
#if 0
CompatibleHashRange<BufferIt> hasher;
CompatibleEqualsRange<BufferIt> equals;
for (BufferIt seg_start(segment.end() - min_length); ; --seg_start) {
NGramMap::const_iterator found = ref_grams.find(SegmentRange(seg_start, segment.end()));
if (found == ref_grams.end()) break;
++counters[found->second];
if (seg_start == segment.begin()) break;
}
#endif
}
} // namespace
class TrombleLossComputerImpl {
public:
explicit TrombleLossComputerImpl(const std::string &params) : star_(TD::Convert("<{STAR}>")) {
typedef boost::tokenizer<boost::char_separator<char> > Tokenizer;
// Argument parsing
std::string ref_file_name;
Tokenizer tok(params, boost::char_separator<char>(" "));
Tokenizer::iterator i = tok.begin();
if (i == tok.end()) {
std::cerr << "TrombleLossComputer needs a reference file name." << std::endl;
exit(1);
}
ref_file_name = *i++;
if (i == tok.end()) {
std::cerr << "TrombleLossComputer needs to know how many references." << std::endl;
exit(1);
}
num_refs_ = boost::lexical_cast<unsigned int>(*i++);
for (; i != tok.end(); ++i) {
thetas_.push_back(boost::lexical_cast<double>(*i));
}
if (thetas_.empty()) {
std::cerr << "TrombleLossComputer is pointless with no weight on n-grams." << std::endl;
exit(1);
}
// Read references file.
std::ifstream ref_file(ref_file_name.c_str());
if (!ref_file) {
std::cerr << "Could not open TrombleLossComputer file " << ref_file_name << std::endl;
exit(1);
}
std::string ref;
vector<vector<WordID> > references(num_refs_);
bound_ngram_id_ = 0;
for (unsigned int sentence = 0; ref_file; ++sentence) {
for (unsigned int refidx = 0; refidx < num_refs_; ++refidx) {
if (!getline(ref_file, ref)) {
if (refidx == 0) break;
std::cerr << "Short read of " << refidx << " references for sentence " << sentence << std::endl;
exit(1);
}
TD::ConvertSentence(ref, &references[refidx]);
}
ref_ids_.resize(sentence + 1);
ref_counts_.resize(sentence + 1);
MakeNGramMapFromReferences(references, thetas_.size(), &ref_counts_.back(), &ref_ids_.back());
bound_ngram_id_ = std::max(bound_ngram_id_, ref_ids_.back().size());
}
}
size_t StateSize() const {
// n-1 boundary words plus counts for n-grams currently rendered as bytes even though most would fit in bits.
// Also, this is cached by higher up classes so no need to cache here.
return MutableState::Size(thetas_.size(), bound_ngram_id_);
}
double Traversal(
const SentenceMetadata &smeta,
const TRule &rule,
const vector<const void*> &ant_contexts,
void *out_context) const {
// TODO: get refs from sentence metadata.
// This will require resizable features.
if (smeta.GetSentenceID() >= ref_ids_.size()) {
std::cerr << "Sentence ID " << smeta.GetSentenceID() << " doesn't have references; there are only " << ref_ids_.size() << " references." << std::endl;
exit(1);
}
const NGramMap &ngrams = ref_ids_[smeta.GetSentenceID()];
MutableState out_state(out_context, thetas_.size());
memset(out_state.counts, 0, bound_ngram_id_ * sizeof(GramCount));
boost::circular_buffer<WordID> history(thetas_.size());
std::vector<const void*>::const_iterator ant_context = ant_contexts.begin();
*out_state.length = 0;
size_t pushed = 0;
const size_t keep = thetas_.size() - 1;
for (vector<WordID>::const_iterator rhs = rule.e().begin(); rhs != rule.e().end(); ++rhs) {
if (*rhs < 1) {
assert(ant_context != ant_contexts.end());
// Constituent
ConstState rhs_state(*ant_context, thetas_.size());
*out_state.length += *rhs_state.length;
{
GramCount *accum = out_state.counts;
for (const GramCount *c = rhs_state.counts; c != rhs_state.counts + ngrams.size(); ++c, ++accum) {
*accum += *c;
}
}
const WordID *w = rhs_state.left;
bool long_constit = true;
for (size_t i = 1; i <= keep; ++i, ++w) {
if (*w == star_) {
long_constit = false;
break;
}
history.push_back(*w);
if (++pushed == keep) {
std::copy(history.begin(), history.end(), out_state.left);
}
// Now i is the length of the history coming from this constituent. So it needs at least i+1 words to have a cross-child add.
AddWord(history, i + 1, ngrams, out_state.counts);
}
// If the consituent is shorter than thetas_.size(), then the
// constituent's left is the entire constituent, so history is already
// correct. Otherwise, the entire right hand side is the entire
// history.
if (long_constit) {
history.assign(thetas_.size(), rhs_state.right, rhs_state.right + keep);
}
++ant_context;
} else {
// Word
++*out_state.length;
history.push_back(*rhs);
if (++pushed == keep) {
std::copy(history.begin(), history.end(), out_state.left);
}
AddWord(history, 1, ngrams, out_state.counts);
}
}
// Fill in left and right constituents.
if (pushed < keep) {
std::copy(history.begin(), history.end(), out_state.left);
for (WordID *i = out_state.left + pushed; i != out_state.left + keep; ++i) {
*i = star_;
}
std::copy(out_state.left, out_state.left + keep, out_state.right);
} else if(pushed == keep) {
std::copy(history.begin(), history.end(), out_state.right);
} else if ((pushed > keep) && !history.empty()) {
std::copy(history.begin() + 1, history.end(), out_state.right);
}
std::vector<RefCounts>::const_iterator ref_info = ref_counts_[smeta.GetSentenceID()].begin();
// Clip the counts and count matches.
// Indexed by reference then by length.
std::vector<std::vector<unsigned int> > matches(num_refs_, std::vector<unsigned int>(thetas_.size()));
for (GramCount *c = out_state.counts; c != out_state.counts + ngrams.size(); ++c, ++ref_info) {
*c = std::min(*c, ref_info->max);
if (*c) {
for (unsigned int refidx = 0; refidx < num_refs_; ++refidx) {
assert(ref_info->length >= 1);
assert(ref_info->length - 1 < thetas_.size());
matches[refidx][ref_info->length - 1] += std::min(*c, ref_info->refs[refidx]);
}
}
}
double best_score = 0.0;
for (unsigned int refidx = 0; refidx < num_refs_; ++refidx) {
double score = 0.0;
for (unsigned int j = 0; j < std::min(*out_state.length, thetas_.size()); ++j) {
score += thetas_[j] * static_cast<double>(matches[refidx][j]) / static_cast<double>(*out_state.length - j);
}
best_score = std::max(best_score, score);
}
return best_score;
}
private:
unsigned int num_refs_;
// Indexed by sentence id.
std::vector<NGramMap> ref_ids_;
// Then by id from ref_ids_.
std::vector<std::vector<RefCounts> > ref_counts_;
// thetas_[0] is the weight for 1-grams
std::vector<double> thetas_;
// All ngram ids in ref_ids_ are < this value.
size_t bound_ngram_id_;
const WordID star_;
};
TrombleLossComputer::TrombleLossComputer(const std::string &params) :
boost::base_from_member<PImpl>(new TrombleLossComputerImpl(params)),
FeatureFunction(boost::base_from_member<PImpl>::member->StateSize()),
fid_(FD::Convert("TrombleLossComputer")) {}
TrombleLossComputer::~TrombleLossComputer() {}
void TrombleLossComputer::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* out_context) const {
(void) estimated_features;
const double loss = boost::base_from_member<PImpl>::member->Traversal(smeta, *edge.rule_, ant_contexts, out_context);
features->set_value(fid_, loss);
}
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