def _wiki2vec_getitem(self, idx):
if self.use_fast_sampler:
if len(self._mention_infos) == 0: self._next_batch()
idx = next(iter(self._mention_infos.keys()))
if idx not in self._mention_infos:
self._next_batch()
mention_info = self._mention_infos.pop(idx)
bag_of_nouns = self._bag_of_nouns_lookup[mention_info['page_id']]
label = self.entity_label_lookup[mention_info['entity_id']]
candidate_ids = self._get_candidate_ids(mention_info['mention'], label)
p_prior = get_p_prior(self.entity_candidates_prior,
mention_info['mention'], candidate_ids)
candidates = self._get_candidate_strs(candidate_ids.tolist())
sample = {
'bag_of_nouns':
bag_of_nouns,
'label':
label,
'p_prior':
p_prior,
'candidate_ids':
candidate_ids,
'candidate_mention_sim':
torch.tensor([
Levenshtein.ratio(mention_info['mention'], candidate)
for candidate in candidates
])
}
self._mentions_per_page_ctr[mention_info['page_id']] -= 1
if self._mentions_per_page_ctr[mention_info['page_id']] == 0:
self._bag_of_nouns_lookup.pop(mention_info['page_id'])
return sample
def _get_batch_entity_page_mentions_lookup(self, page_ids):
lookup = {}
page_mention_infos_lookup = defaultdict(list)
for mention_info in self._mention_infos.values():
page_mention_infos_lookup[mention_info['page_id']].append(
mention_info)
for page_id in page_ids:
page_mention_infos = page_mention_infos_lookup[page_id]
entity_ids_for_page = []
for mention_info in page_mention_infos:
mention = mention_info['mention']
if self.entity_candidates_prior.get(mention) is None:
approx_mentions = self.prior_approx_mapping.get(
unidecode.unidecode(mention).lower(), [])
candidate_ids = list(
set(
sum([
list(
self.entity_candidates_prior.get(
approx_mention, {}).keys())
for approx_mention in approx_mentions
], [])))
else:
candidate_ids = list(
self.entity_candidates_prior[mention].keys())
prior = get_p_prior(self.entity_candidates_prior,
self.prior_approx_mapping,
mention_info['mention'],
torch.tensor(candidate_ids))
if len(prior) > 0:
most_common_idx = int(torch.argmax(prior))
entity_ids_for_page.append(candidate_ids[most_common_idx])
lookup[page_id] = torch.tensor(
entity_ids_for_page, device=self.entity_embeds.weight.device)
return lookup
def _getitem(self, idx):
if self.use_fast_sampler:
if len(self._mention_infos) == 0: self._next_batch()
idx = next(iter(self._mention_infos.keys()))
if idx not in self._mention_infos:
self._next_batch()
mention_info = self._mention_infos.pop(idx)
sentence_spans = self._sentence_spans_lookup[mention_info['page_id']]
page_content = self._page_content_lookup[mention_info['page_id']]
label = self.entity_label_lookup[mention_info['entity_id']]
candidate_ids = self._get_candidate_ids(mention_info['mention'], label)
p_prior = get_p_prior(self.entity_candidates_prior,
self.prior_approx_mapping,
mention_info['mention'], candidate_ids)
candidates = self._get_candidate_strs(candidate_ids.tolist())
sample = {
'sentence_splits':
get_mention_sentence_splits(page_content,
sentence_spans,
mention_info,
lim=self.page_content_lim),
'label':
label,
'embedded_page_content':
self._embedded_page_content_lookup[mention_info['page_id']],
'entity_page_mentions':
self.entity_embeds(
self._entity_page_mentions_lookup[mention_info['page_id']]),
'p_prior':
p_prior,
'candidate_ids':
candidate_ids,
'candidate_mention_sim':
torch.tensor([
Levenshtein.ratio(mention_info['mention'], candidate)
for candidate in candidates
])
}
self._mentions_per_page_ctr[mention_info['page_id']] -= 1
if self._mentions_per_page_ctr[mention_info['page_id']] == 0:
self._sentence_spans_lookup.pop(mention_info['page_id'])
self._page_content_lookup.pop(mention_info['page_id'])
self._embedded_page_content_lookup.pop(mention_info['page_id'])
self._entity_page_mentions_lookup.pop(mention_info['page_id'])
return sample
def _getitem_sum_encoder(self, idx):
idx = self.with_label[idx]
label = self.entity_label_lookup.get(self.labels[idx], -1)
mention = self.mentions[idx]
candidate_ids = get_candidate_ids(self.entity_candidates_prior,
self.prior_approx_mapping,
self.num_entities,
self.num_candidates,
mention,
label)
candidates = get_candidate_strs(self.cursor, [self.entity_id_lookup[cand_id] for cand_id in candidate_ids.tolist()])
return {'mention_sentence': self.mention_sentences[idx],
'page_token_cnts': self.page_token_cnts_lookup[self.mention_doc_id[idx]],
'label': label,
'p_prior': get_p_prior(self.entity_candidates_prior, self.prior_approx_mapping, mention, candidate_ids),
'candidate_ids': candidate_ids,
'candidate_mention_sim': torch.tensor([Levenshtein.ratio(mention, candidate)
for candidate in candidates])}
def _getitem_wiki2vec(self, idx):
idx = self.with_label[idx]
label = self.entity_label_lookup.get(self.labels[idx], -1)
mention = self.mentions[idx]
candidate_ids = get_candidate_ids(self.entity_candidates_prior,
self.prior_approx_mapping,
self.num_entities,
self.num_candidates,
mention,
label)
bag_of_nouns = get_bag_of_nouns(self.documents[self.mention_doc_id[idx]])
candidates = get_candidate_strs(self.cursor, [self.entity_id_lookup[cand_id] for cand_id in candidate_ids.tolist()])
return {'label': label,
'bag_of_nouns': bag_of_nouns,
'p_prior': get_p_prior(self.entity_candidates_prior, self.prior_approx_mapping, mention, candidate_ids),
'candidate_ids': candidate_ids,
'candidate_mention_sim': torch.tensor([Levenshtein.ratio(mention, candidate)
for candidate in candidates])}
def __getitem__(self, idx):
idx = self.with_label[idx]
label = self.entity_label_lookup.get(self.labels[idx]) or -1
mention = self.mentions[idx]
candidate_ids = get_candidate_ids(self.entity_candidates_prior,
self.num_entities,
self.num_candidates,
mention,
label)
candidates = get_candidate_strs(self.cursor, [self.entity_id_lookup[cand_id] for cand_id in candidate_ids.tolist()])
return {'sentence_splits': self.sentence_splits[idx],
'label': label,
'embedded_page_content': self.embedded_documents[self.mention_doc_id[idx]],
'entity_page_mentions': embed_page_content(self.embedding,
self.token_idx_lookup,
' '.join(self.mentions_by_doc_id[self.mention_doc_id[idx]])),
'p_prior': get_p_prior(self.entity_candidates_prior, mention, candidate_ids),
'candidate_ids': candidate_ids,
'candidate_mention_sim': torch.tensor([Levenshtein.ratio(mention, candidate)
for candidate in candidates])}
