// Decode the label marginals for each candidate arc. The output vector
// total_scores contains the sum of exp-scores (over the labels) for each arc;
// label_marginals contains those marginals ignoring the tree constraint.
void ConstituencyLabelerDecoder::DecodeLabelMarginals(
Instance *instance, Parts *parts,
const std::vector<double> &scores,
std::vector<double> *total_scores,
std::vector<double> *label_marginals) {
ConstituencyLabelerInstanceNumeric *sentence =
static_cast<ConstituencyLabelerInstanceNumeric*>(instance);
ConstituencyLabelerParts *labeled_parts =
static_cast<ConstituencyLabelerParts*>(parts);
ConstituencyLabelerOptions *labeler_options =
static_cast<ConstituencyLabelerOptions*>(pipe_->GetOptions());
int num_nodes = sentence->GetNumConstituents();
int offset_labeled_nodes, num_labeled_nodes;
labeled_parts->GetOffsetNode(&offset_labeled_nodes, &num_labeled_nodes);
total_scores->clear();
total_scores->resize(num_nodes, 0.0);
label_marginals->clear();
label_marginals->resize(num_labeled_nodes, 0.0);
for (int i = 0; i < num_nodes; ++i) {
const std::vector<int> &index_node_parts =
labeled_parts->FindNodeParts(i);
// If no part for null label, initiliaze log partition to exp(0.0) to
// account the null label which has score 0.0.
LogValD total_score = (labeler_options->ignore_null_labels()) ?
LogValD::One() : LogValD::Zero();
for (int k = 0; k < index_node_parts.size(); ++k) {
total_score += LogValD(scores[index_node_parts[k]], false);
}
(*total_scores)[i] = total_score.logabs();
// If no part for null label, initiliaze sum to exp(0.0)/Z to
// account the null label which has score 0.0.
double sum = (labeler_options->ignore_null_labels()) ?
(1.0 / total_score.as_float()) : 0.0;
for (int k = 0; k < index_node_parts.size(); ++k) {
LogValD marginal =
LogValD(scores[index_node_parts[k]], false) / total_score;
(*label_marginals)[index_node_parts[k] - offset_labeled_nodes] =
marginal.as_float();
sum += marginal.as_float();
}
if (!NEARLY_EQ_TOL(sum, 1.0, 1e-9)) {
LOG(INFO) << "Label marginals don't sum to one: sum = " << sum;
}
}
}
// Compute marginals and evaluate log partition function for a coreference tree
// model.
void CoreferenceDecoder::DecodeBasicMarginals(
Instance *instance, Parts *parts,
const std::vector<double> &scores,
std::vector<double> *predicted_output,
double *log_partition_function,
double *entropy) {
CoreferenceDocumentNumeric *document =
static_cast<CoreferenceDocumentNumeric*>(instance);
CoreferenceParts *coreference_parts = static_cast<CoreferenceParts*>(parts);
predicted_output->clear();
predicted_output->resize(parts->size(), 0.0);
*log_partition_function = 0.0;
*entropy = 0.0;
const std::vector<Mention*> &mentions = document->GetMentions();
for (int j = 0; j < mentions.size(); ++j) {
// List all possible antecedents and pick the one with highest score.
const std::vector<int> &arcs = coreference_parts->FindArcParts(j);
int best_antecedent = -1;
// Find the best label for each candidate arc.
LogValD total_score = LogValD::Zero();
//LOG(INFO) << "num_arcs = " << arcs.size();
for (int k = 0; k < arcs.size(); ++k) {
int r = arcs[k];
total_score += LogValD(scores[r], false);
//LOG(INFO) << "scores[" << r << "] = " << scores[r];
}
//LOG(INFO) << "total score = " << total_score.logabs();
*log_partition_function += total_score.logabs();
double sum = 0.0;
for (int k = 0; k < arcs.size(); ++k) {
int r = arcs[k];
LogValD marginal = LogValD(scores[r], false) / total_score;
double marginal_value = marginal.as_float();
(*predicted_output)[r] = marginal_value;
#if 0
if (marginal_value > 0.0) {
LOG(INFO) << "Marginal[" << j << ", "
<< static_cast<CoreferencePartArc*>((*parts)[r])->parent_mention()
<< "] = " << marginal_value;
}
#endif
if (scores[r] != -std::numeric_limits<double>::infinity()) {
*entropy -= scores[r] * marginal_value;
} else {
CHECK_EQ(marginal_value, 0.0);
}
sum += marginal_value;
}
if (!NEARLY_EQ_TOL(sum, 1.0, 1e-9)) {
LOG(INFO) << "Antecedent marginals don't sum to one: sum = " << sum;
}
}
*entropy += *log_partition_function;
#if 0
LOG(INFO) << "Log-partition function: " << *log_partition_function;
LOG(INFO) << "Entropy: " << *entropy;
#endif
}
