def init_vocab(self, emb_configs, vocab_size):
# Load several pretrained embeddings and concatenate them.
pretrained = [self.load(c['path'], vocab_size, c['size'], c['skip_first']) for c in emb_configs]
rev_vocab = common.flatten([list(e.keys()) for e in pretrained])
rev_vocab = self.start_vocab + rev_vocab[:vocab_size]
vocab = collections.OrderedDict()
for i,t in enumerate(rev_vocab):
vocab[t] = i
# Merge pretrained embeddings.
if self.normalize_embedding:
# Normalize the pretrained embeddings for each of the embedding types.
embeddings = [common.flatten([common.normalize_vector(emb[w]) for emb in pretrained]) for w in vocab]
else:
embeddings = [common.flatten([emb[w] for emb in pretrained]) for w in vocab]
# tokens in START_VOCAB are randomly initialized.
#rand_gen = random_embedding_generator(len(embeddings[0]))
#for i in range(len(self.start_vocab)):
# embeddings[i] = rand_gen()
embeddings = np.array(embeddings)
sys.stderr.write("Done loading word embeddings.\n")
return vocab, rev_vocab, embeddings
def read_text(text, tokenizer):
if isinstance(text, list):
assert type(text[0]) == str
words = flatten(tokenizer(sent) for sent in text)
else:
words = tokenizer(text)
return words
def merge(self,
pretrained,
vocab_size,
vocab_merge_type='union',
embedding_merge_type='first_found'):
'''
<Args>
- pretrained: A list of dictionary, {word: vector}.
- vocab_merge_type: ['union', 'intersection']
- embedding_merge_type: ['first_found', 'average', 'concat']
'''
if vocab_merge_type != 'union':
raise NotImplementedError
if embedding_merge_type != 'first_found':
raise NotImplementedError
rev_vocab = list(
OrderedSet(common.flatten([list(v.keys())
for v in pretrained]))) # union.
embedding_size = len(pretrained[0][rev_vocab[0]])
embedding_dict = defaultdict(zero_embedding_generator(embedding_size))
for w in rev_vocab:
embedding_dict[w] = [vecs[w] for vecs in pretrained
if w in vecs][0]
return embedding_dict
def decorate_text(self_class, example, vocab, prediction=None):
'''
Args:
- example: A recDotDefaultDict, one example of a flattened batch.
Refer to WikiP2DRelExDataset.article2entries.
'''
text = copy.deepcopy(example.text.flat)
query = example.query
for i, w in enumerate(text):
if vocab.word.is_unk(w):
text[i] = UNDERLINE + text[i]
query_positions = set([
j
for j in range(query.flat_position[0], query.flat_position[1] +
1)
])
gold_mention_positions = set(
flatten([[j for j in range(begin, end + 1)]
for begin, end in example.mentions.flat_position]))
if PAD_ID in gold_mention_positions:
gold_mention_positions.remove(PAD_ID)
if i in query_positions:
text[i] = MAGENTA + text[i]
if i in gold_mention_positions:
text[i] = BLUE + text[i]
text[i] = text[i] + RESET
return text #'\n'.join([' '.join(sent) for sent in text])
def get_input_feed(self, batch, is_training):
input_feed = {}
## Texts
if self.encoder.cbase:
input_feed[self.ph.text.char] = batch.text.char
if self.encoder.wbase:
input_feed[self.ph.text.word] = batch.text.word
input_feed[self.ph.sentence_length] = batch.sentence_length
## Mention spans and their clusters
gold_mentions = sorted(tuple(m) for m in flatten(batch.clusters))
gold_mention_map = {m: i for i, m in enumerate(gold_mentions)}
cluster_ids = np.zeros(len(gold_mentions))
for cluster_id, cluster in enumerate(batch.clusters):
for mention in cluster:
cluster_ids[gold_mention_map[tuple(mention)]] = cluster_id
gold_starts, gold_ends = self.tensorize_mentions(gold_mentions)
input_feed[self.ph.gold_starts] = np.array(gold_starts)
input_feed[self.ph.gold_ends] = np.array(gold_ends)
input_feed[self.ph.cluster_ids] = np.array(cluster_ids)
## Metadata
input_feed[self.is_training] = is_training
input_feed[self.ph.speaker_ids] = batch.speakers
input_feed[self.ph.genre] = batch.genre
if is_training and batch.text.word.shape[
0] > self.max_training_sentences:
return self.truncate_example(input_feed)
else:
return input_feed
def preprocess(self, article):
raw_text = [s.split() for s in article.text]
num_words = [len(s) for s in raw_text]
article.text = raw_text
article.flat_text = flatten(raw_text)
article.desc = article.desc.split()
article.num_words = sum([len(s) for s in article.text])
return article
def main(args):
word_embs = read_embedding(args.source_emb)
data = read_jsonlines(args.dataset_path, max_rows=0)
tokenizer = word_tokenizer(args.lowercase, args.normalize_digits)
words = flatten([read_text(d.text, tokenizer) for d in data])
word_freq = sorted(Counter(words).items(), key=lambda x: -x[1])
for word, freq in word_freq:
if word in word_embs:
line = [word] + word_embs[word]
line = ' '.join([str(x) for x in line])
print(line)
def _tokenizer(sent, flatten=False):
if normalize_digits:
sent = re.sub(_DIGIT_RE, "0", sent)
if lowercase:
sent = sent.lower()
def word2chars(word):
return [c for c in word]
words = sent.replace('\n', '').split()
chars = [word2chars(w) for w in words]
if flatten:
chars = common.flatten(chars)
return chars
def main(args):
tokenizer = word_tokenizer(args.lowercase, args.normalize_digits,
separative_tokens=['-', '/'])
data = read_jsonlines(args.descdata_path)
word_freq = OrderedDict(sorted([(k, freq) for k, freq in Counter(flatten([tokenizer(d.desc) for d in data])).items()], key=lambda x: -x[1]))
embedding_dict = read_pretrained_emb(word_freq, tokenizer)
with open(args.emb_target_path, 'w') as f:
for w, v in embedding_dict.items():
if not v:
continue
line = "%s %s\n" % (w, ' '.join([str(x) for x in v]))
f.write(line)
def setup_tokenizer(tokenizer_type=None):
assert tokenizer_type is None or tokenizer_type in ['corenlp', 'nltk']
if tokenizer_type == 'corenlp':
#from core.utils.tokenizer import connect_to_corenlp_server, run_corenlp
corenlp = connect_to_corenlp_server(host='http://localhost', port=9000)
tokenizer = lambda uttr: flatten(run_corenlp(uttr, corenlp))
elif tokenizer_type == 'nltk':
from nltk import word_tokenize
tokenizer = word_tokenize
else:
tokenizer = lambda uttr: uttr.split()
# tokenizer must return a list of words.
return tokenizer
def preprocess(self, article):
raw_text = [s.split() for s in article.text]
num_words = [len(s) for s in raw_text]
links = {}
# Convert a list of sentneces to a flattened sequence of words.
for qid, link in article.link.items():
(sent_id, (begin, end)) = link
flatten_begin = begin + sum(num_words[:sent_id])
flatten_end = end + sum(num_words[:sent_id])
assert flatten_begin >= 0 and flatten_end >= 0
links[qid] = (flatten_begin, flatten_end)
article.link = links
article.text = flatten(raw_text)
article.desc = article.desc.split()
return article
def _tokenizer(sent, flatten=False):
if split_quotation:
sent = sent.replace("'", " ' ")
if normalize_digits:
sent = re.sub(_DIGIT_RE, "0", sent)
if lowercase:
sent = sent.lower()
def word2chars(word):
return [c for c in word]
words = sent.replace('\n', '')
words = words.split() if not use_nltk_tokenizer else word_tokenize(
words)
chars = [word2chars(w) for w in words]
if flatten:
chars = common.flatten(chars)
return chars
def debug(self, model=None):
if not model:
model = self.create_model(self.config)
self.output_variables_as_text(model)
exit(1)
batch = common.recDotDefaultDict()
state = [common.flatten([[1, 0, 0, 0] for _ in range(160)])]
batch.state = state
batch.is_sente = [[1, 0] for _ in state]
batch.current_num_cards = [[1, 1] for s in state]
batch.is_training = False
res = model.step(batch, 0)
print(res)
batches = self.dataset.get_batches(self.config.batch_size,
0,
is_training=True)
for b in batches:
b = common.flatten_recdict(b)
for k in b:
print(k, b[k])
exit(1)
def test(self, batches, mode, logger, output_path):
results = []
used_batches = []
for i, batch in enumerate(batches):
input_feed = self.get_input_feed(batch, False)
outputs = self.sess.run(self.predictions, input_feed)
try:
used_batches += flatten_batch(batch)
except Exception as e:
pprint(batch)
print(e)
exit(1)
results.append(outputs[:, 0, :])
results = flatten([r.tolist() for r in results])
sys.stdout = open(output_path, 'w') if output_path else sys.stdout
bleu = evaluate_and_print(used_batches, results, vocab=self.vocab)
if output_path:
sys.stderr.write(
"Output the testing results to \'{}\' .\n".format(output_path))
sys.stdout = sys.__stdout__
summary_dict = {}
summary_dict['desc/%s/BLEU' % mode] = bleu
summary = make_summary(summary_dict)
return bleu, summary
def load_data(self):
sys.stderr.write("Loading wikiP2D dataset from \'%s\'... \n" %
self.source_path)
data = read_jsonlines(self.source_path, max_rows=self.max_rows)
data = [self.preprocess(d) for d in data]
self.data = flatten([self.article2entries(d) for d in data])
def article2entries(self, article):
def qid2entity(qid, article):
assert qid in article.link
s_id, (begin, end) = article.link[qid]
# The offset is the number of words in previous sentences.
offset = sum([len(sent) for sent in article.text[:s_id]])
entity = recDotDefaultDict()
# Replace entity's name with the actual representation in the article.
entity.raw = ' '.join(article.text[s_id][begin:end + 1])
entity.position = article.link[qid]
entity.flat_position = (begin + offset, end + offset)
return entity
entry = recDotDefaultDict()
entry.qid = article.qid
entry.text.raw = article.text
entry.text.flat = article.flat_text
entry.text.word = [self.vocab.word.sent2ids(s) for s in article.text]
entry.text.char = [self.vocab.char.sent2ids(s) for s in article.text]
entry.query = qid2entity(article.qid, article) # (begin, end)
# Articles which contain triples less than self.min_triples are discarded since they can be incorrect.
if len(article.triples.subjective.ids) + len(
article.triples.objective.ids) < self.min_triples:
return []
entry.mentions.raw = []
entry.mentions.flat_position = []
for t_type in ['subjective', 'objective']:
entry.triples[t_type] = []
entry.target[t_type] = [[
self.vocab.rel.UNK_ID for j in range(self.max_mention_width)
] for i in range(article.num_words)]
for triple_idx, triple in enumerate(
article.triples[t_type].ids): # triple = [subj, rel, obj]
is_subjective = triple[0] == article.qid
query_qid, rel_pid, mention_qid = triple if is_subjective else reversed(
triple)
# TODO: 同じメンションがクエリと異なる関係を持つ場合は？
mention = qid2entity(mention_qid, article)
#entry.mentions[t_type].raw.append(mention.raw)
#entry.mentions[t_type].flat_position.append(mention.flat_position)
entry.mentions.raw.append(mention.raw)
entry.mentions.flat_position.append(mention.flat_position)
rel = dotDict({
'raw': rel_pid,
'name': self.vocab.rel.token2name(rel_pid)
})
begin, end = mention.flat_position
if end - begin < self.max_mention_width:
entry.target[t_type][begin][
end - begin] = self.vocab.rel.token2id(rel_pid)
triple = [entry.query, rel, mention
] if is_subjective else [mention, rel, entry.query]
entry.triples[t_type].append(triple)
relation_freqs = Counter(flatten(entry.target.subjective))
# TODO: For now this experiments focus only on subjective relations.
entry.triples.objective = []
#####################
entry.loss_weights_by_label = [1.0 for _ in range(self.vocab.rel.size)]
entry.num_mentions = len(entry.mentions.flat_position)
return [entry]
