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#include "ff_spans.h"
#include <sstream>
#include <cassert>
#include <cmath>
#include "hg.h"
#include "tdict.h"
#include "filelib.h"
#include "stringlib.h"
#include "sentence_metadata.h"
#include "lattice.h"
#include "fdict.h"
#include "verbose.h"
using namespace std;
namespace {
string Escape(const string& x) {
string y = x;
for (int i = 0; i < y.size(); ++i) {
if (y[i] == '=') y[i]='_';
if (y[i] == ';') y[i]='_';
}
return y;
}
}
// log transform to make long spans cluster together
// but preserve differences
int SpanSizeTransform(unsigned span_size) {
if (!span_size) return 0;
return static_cast<int>(log(span_size+1) / log(1.39)) - 1;
}
SpanFeatures::SpanFeatures(const string& param) :
kS(TD::Convert("S") * -1),
kX(TD::Convert("X") * -1),
use_collapsed_features_(false) {
string mapfile = param;
string valfile;
vector<string> toks;
Tokenize(param, ' ', &toks);
if (toks.size() == 2) { mapfile = toks[0]; valfile = toks[1]; }
if (mapfile.size() > 0) {
int lc = 0;
if (!SILENT) { cerr << "Reading word map for SpanFeatures from " << param << endl; }
ReadFile rf(mapfile);
istream& in = *rf.stream();
string line;
vector<WordID> v;
while(in) {
++lc;
getline(in, line);
if (line.empty()) continue;
v.clear();
TD::ConvertSentence(line, &v);
if (v.size() != 2) {
cerr << "Error reading line " << lc << ": " << line << endl;
abort();
}
word2class_[v[0]] = v[1];
}
word2class_[TD::Convert("BOS")] = TD::Convert("BOS");
word2class_[TD::Convert("EOS")] = TD::Convert("EOS");
oov_ = TD::Convert("OOV");
}
if (valfile.size() > 0) {
use_collapsed_features_ = true;
fid_beg_ = FD::Convert("SpanBegin");
fid_end_ = FD::Convert("SpanEnd");
fid_span_s_ = FD::Convert("SSpanContext");
fid_span_ = FD::Convert("XSpanContext");
ReadFile rf(valfile);
if (!SILENT) { cerr << " Loading span scores from " << valfile << endl; }
istream& in = *rf.stream();
string line;
while(in) {
getline(in, line);
if (line.size() == 0 || line[0] == '#') { continue; }
istringstream in(line);
string feat_name;
double weight;
in >> feat_name >> weight;
feat2val_[feat_name] = weight;
}
}
}
void SpanFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const {
assert(edge.j_ < end_span_ids_.size());
assert(edge.j_ >= 0);
assert(edge.i_ < beg_span_ids_.size());
assert(edge.i_ >= 0);
if (use_collapsed_features_) {
features->set_value(fid_end_, end_span_vals_[edge.j_]);
features->set_value(fid_beg_, beg_span_vals_[edge.i_]);
if (edge.rule_->lhs_ == kS)
features->set_value(fid_span_s_, span_vals_(edge.i_,edge.j_).second);
else
features->set_value(fid_span_, span_vals_(edge.i_,edge.j_).first);
} else { // non-collapsed features:
features->set_value(end_span_ids_[edge.j_], 1);
features->set_value(beg_span_ids_[edge.i_], 1);
features->set_value(end_bigram_ids_[edge.j_], 1);
features->set_value(beg_bigram_ids_[edge.i_], 1);
if (edge.rule_->lhs_ == kS) {
features->set_value(span_feats_(edge.i_,edge.j_).second, 1);
features->set_value(len_span_feats_(edge.i_,edge.j_).second, 1);
} else {
features->set_value(span_feats_(edge.i_,edge.j_).first, 1);
features->set_value(len_span_feats_(edge.i_,edge.j_).first, 1);
}
}
}
WordID SpanFeatures::MapIfNecessary(const WordID& w) const {
if (word2class_.empty()) return w;
map<WordID,WordID>::const_iterator it = word2class_.find(w);
if (it == word2class_.end()) return oov_;
return it->second;
}
void SpanFeatures::PrepareForInput(const SentenceMetadata& smeta) {
const Lattice& lattice = smeta.GetSourceLattice();
const WordID eos = TD::Convert("EOS"); // right of the last source word
const WordID bos = TD::Convert("BOS"); // left of the first source word
beg_span_ids_.resize(lattice.size() + 1);
end_span_ids_.resize(lattice.size() + 1);
span_feats_.resize(lattice.size() + 1, lattice.size() + 1);
beg_bigram_ids_.resize(lattice.size() + 1);
end_bigram_ids_.resize(lattice.size() + 1);
len_span_feats_.resize(lattice.size() + 1, lattice.size() + 1);
if (use_collapsed_features_) {
beg_span_vals_.resize(lattice.size() + 1);
end_span_vals_.resize(lattice.size() + 1);
span_vals_.resize(lattice.size() + 1, lattice.size() + 1);
}
for (int i = 0; i <= lattice.size(); ++i) {
WordID word = eos;
WordID bword = bos;
if (i > 0)
bword = lattice[i-1][0].label;
bword = MapIfNecessary(bword);
if (i < lattice.size())
word = lattice[i][0].label; // rather arbitrary for lattices
word = MapIfNecessary(word);
ostringstream sfid;
sfid << "ES:" << TD::Convert(word);
end_span_ids_[i] = FD::Convert(Escape(sfid.str()));
ostringstream esbiid;
esbiid << "EBI:" << TD::Convert(bword) << "_" << TD::Convert(word);
end_bigram_ids_[i] = FD::Convert(Escape(esbiid.str()));
ostringstream bsbiid;
bsbiid << "BBI:" << TD::Convert(bword) << "_" << TD::Convert(word);
beg_bigram_ids_[i] = FD::Convert(Escape(bsbiid.str()));
ostringstream bfid;
bfid << "BS:" << TD::Convert(bword);
beg_span_ids_[i] = FD::Convert(Escape(bfid.str()));
if (use_collapsed_features_) {
end_span_vals_[i] = feat2val_[Escape(sfid.str())] + feat2val_[Escape(esbiid.str())];
beg_span_vals_[i] = feat2val_[Escape(bfid.str())] + feat2val_[Escape(bsbiid.str())];
}
}
for (int i = 0; i <= lattice.size(); ++i) {
WordID bword = bos;
if (i > 0)
bword = lattice[i-1][0].label;
bword = MapIfNecessary(bword);
for (int j = 0; j <= lattice.size(); ++j) {
WordID word = eos;
if (j < lattice.size())
word = lattice[j][0].label;
word = MapIfNecessary(word);
ostringstream pf;
pf << "S:" << TD::Convert(bword) << "_" << TD::Convert(word);
span_feats_(i,j).first = FD::Convert(Escape(pf.str()));
span_feats_(i,j).second = FD::Convert(Escape("S_" + pf.str()));
ostringstream lf;
const unsigned span_size = (i < j ? j - i : i - j);
lf << "LS:" << SpanSizeTransform(span_size) << "_" << TD::Convert(bword) << "_" << TD::Convert(word);
len_span_feats_(i,j).first = FD::Convert(Escape(lf.str()));
len_span_feats_(i,j).second = FD::Convert(Escape("S_" + lf.str()));
if (use_collapsed_features_) {
span_vals_(i,j).first = feat2val_[Escape(pf.str())] + feat2val_[Escape(lf.str())];
span_vals_(i,j).second = feat2val_[Escape("S_" + pf.str())] + feat2val_[Escape("S_" + lf.str())];
}
}
}
}
inline bool IsArity2RuleReordered(const TRule& rule) {
const vector<WordID>& e = rule.e_;
for (int i = 0; i < e.size(); ++i) {
if (e[i] <= 0) { return e[i] < 0; }
}
cerr << "IsArity2RuleReordered failed on:\n" << rule.AsString() << endl;
abort();
}
// Chiang, Marton, Resnik 2008 "fine-grained" reordering features
CMR2008ReorderingFeatures::CMR2008ReorderingFeatures(const std::string& param) :
kS(TD::Convert("S") * -1),
use_collapsed_features_(false) {
if (param.size() > 0) {
use_collapsed_features_ = true;
assert(!"not implemented"); // TODO
} else {
unconditioned_fids_.first = FD::Convert("CMRMono");
unconditioned_fids_.second = FD::Convert("CMRReorder");
fids_.resize(16); fids_[0].first = fids_[0].second = -1;
// since I use a log transform, I go a bit higher than David, who bins everything > 10
for (int span_size = 1; span_size <= 15; ++span_size) {
ostringstream m, r;
m << "CMRMono_" << SpanSizeTransform(span_size);
fids_[span_size].first = FD::Convert(m.str());
r << "CMRReorder_" << SpanSizeTransform(span_size);
fids_[span_size].second = FD::Convert(r.str());
}
}
}
void CMR2008ReorderingFeatures::TraversalFeaturesImpl(const SentenceMetadata& smeta,
const Hypergraph::Edge& edge,
const vector<const void*>& ant_contexts,
SparseVector<double>* features,
SparseVector<double>* estimated_features,
void* context) const {
if (edge.Arity() != 2) return;
if (edge.rule_->lhs_ == kS) return;
assert(edge.i_ >= 0);
assert(edge.j_ > edge.i_);
const bool is_reordered = IsArity2RuleReordered(*edge.rule_);
const unsigned span_size = edge.j_ - edge.i_;
if (use_collapsed_features_) {
assert(!"not impl"); // TODO
} else {
if (is_reordered) {
features->set_value(unconditioned_fids_.second, 1.0);
features->set_value(fids_[span_size].second, 1.0);
} else {
features->set_value(unconditioned_fids_.first, 1.0);
features->set_value(fids_[span_size].first, 1.0);
}
}
}
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