def build_vocab(self, corpus):
"""
Build vocabulary from a sequence of sentences or from a frequency dictionary, if one was provided.
"""
if self.vocabulary_counts != None:
logger.debug("building vocabulary from provided frequency map")
vocab = self.vocabulary_counts
else:
logger.debug("default vocabulary building")
super(Skipgram, self).build_vocab(corpus)
return
# assign a unique index to each word
self.vocab, self.index2word = {}, []
for word, count in vocab.iteritems():
v = Vocab()
v.count = count
if v.count >= self.min_count:
v.index = len(self.vocab)
self.index2word.append(word)
self.vocab[word] = v
logger.debug("total %i word types after removing those with count<%s" %
(len(self.vocab), self.min_count))
if self.hs:
# add info about each word's Huffman encoding
self.create_binary_tree()
if self.negative:
# build the table for drawing random words (for negative sampling)
self.make_table()
# precalculate downsampling thresholds
self.precalc_sampling()
self.reset_weights()
def build_vocab(self, corpus):
"""
Build vocabulary from a sequence of sentences or from a frequency dictionary, if one was provided.
"""
if self.vocabulary_counts != None:
print "building vocabulary from provided frequency map"
vocab = self.vocabulary_counts
else:
print "default vocabulary building"
super(Skipgram, self).build_vocab(corpus)
return
# assign a unique index to each word
self.vocab, self.index2word = {}, []
for word, count in vocab.iteritems():
v = Vocab()
v.count = count
if v.count >= self.min_count:
v.index = len(self.vocab)
self.index2word.append(word)
self.vocab[word] = v
self.corpus_count = len(vocab)
self.raw_vocab = vocab
logger.debug("total %i word types after removing those with count<%s" % (len(self.vocab), self.min_count))
self.scale_vocab()
self.finalize_vocab()
def build_vocab(self, corpus):
"""
Build vocabulary from a sequence of sentences or from a frequency dictionary, if one was provided.
"""
if self.vocabulary_counts != None:
print "building vocabulary from provided frequency map"
vocab = self.vocabulary_counts
else:
print "default vocabulary building"
super(Skipgram, self).build_vocab(corpus)
return
# assign a unique index to each word
self.vocab, self.index2word = {}, []
for word, count in vocab.iteritems():
v = Vocab()
v.count = count
if v.count >= self.min_count:
v.index = len(self.vocab)
self.index2word.append(word)
self.vocab[word] = v
self.corpus_count = len(vocab)
self.raw_vocab = vocab
logger.debug("total %i word types after removing those with count<%s" %
(len(self.vocab), self.min_count))
self.scale_vocab()
self.finalize_vocab()
def build_vocab(self, corpus):
"""
Build vocabulary from a sequence of sentences or from a frequency dictionary, if one was provided.
"""
if self.vocabulary_counts != None:
logger.debug("building vocabulary from provided frequency map")
vocab = self.vocabulary_counts
else:
logger.debug("default vocabulary building")
super(Skipgram, self).build_vocab(corpus)
return
# assign a unique index to each word
self.vocab, self.index2word = {}, []
for word, count in vocab.iteritems():
v = Vocab()
v.count = count
if v.count >= self.min_count:
v.index = len(self.vocab)
self.index2word.append(word)
self.vocab[word] = v
logger.debug("total %i word types after removing those with count<%s" % (len(self.vocab), self.min_count))
if self.hs:
# add info about each word's Huffman encoding
self.create_binary_tree()
if self.negative:
# build the table for drawing random words (for negative sampling)
self.make_table()
# precalculate downsampling thresholds
self.precalc_sampling()
self.reset_weights()
def add_new_labels(sentences, model):
"""
Add new labels (for new docs) to the doc2vec model's `self.vocab`.
from: <https://gist.github.com/zseder/4201551d7f8608f0b82b>
"""
sentence_no = -1
total_words = 0
vocab = model.vocab
#model_sentence_n = len([l for l in vocab if l.startswith("SENT")])
model_sentence_n = max(int(l.split('_')[-1]) for l in vocab if l.startswith("SENT"))
n_sentences = 0
for sentence_no, sentence in enumerate(sentences):
sentence_length = len(sentence.words)
for label in sentence.labels:
label_e = label.split("_")
label_n = int(label_e[1]) + model_sentence_n
label = "{0}_{1}".format(label_e[0], label_n)
total_words += 1
if label in vocab:
vocab[label].count += sentence_length
else:
vocab[label] = Vocab(count=sentence_length)
vocab[label].index = len(model.vocab) - 1
vocab[label].code = [0]
vocab[label].sample_probability = 1.
model.index2word.append(label)
n_sentences += 1
return n_sentences
def build_vocab(self, sentences):
logger.info("collecting all words and their counts")
vocab = self._vocab_from_new(sentences)
# assign a unique index to each word
self.vocab, self.index2word = {}, []
for meta_word in [
self.label0_as_vocab, self.label1_as_vocab,
self.unknown_as_vocab
]:
v = Vocab(count=1)
v.index = len(self.vocab)
v.sample_probability = 1.0
self.index2word.append(meta_word)
self.vocab[meta_word] = v
# remove word with count < min_count, default min_count = 5 in gensim, Seger changed to 1
# actually, not remove any words
# build self.vocab word->Vocab dict, and assign a unique index to each word
for subgram, v in iteritems(vocab):
if v.count >= self.min_count:
v.sample_probability = 1.0
v.index = len(self.vocab)
self.index2word.append(subgram)
self.vocab[subgram] = v
logger.info("total %i word types after removing those with count<%s" %
(len(self.vocab), self.min_count))
logger.info('reset weights')
if self.hybrid_pred:
# v is word
# get single character word frequency
freq_list = [
self.vocab[v].count for v in self.vocab if len(v) == 1
]
freq_list.sort(reverse=True)
self.hybrid_threshold = freq_list[len(freq_list) / 25]
print '>frequencey threshold for hybrid prediction is:', self.hybrid_threshold
self.reset_weights()
def create_corpus_from_matlab(word_embedding, index2word):
model = Word2VecExtended()
model.syn0 = word_embedding.astype(theano.config.floatX).copy()
model.index2word = index2word
model.index2word[0] = UnknownWord
vocab = {}
for word in model.index2word:
v = Vocab(count=1)
v.index = len(vocab)
vocab[word] = v
model.vocab = vocab
model.UnknownWordIndex = model.vocab[UnknownWord].index
return model
def build_vocab(self, sentences):
logger.info("collecting all words and their counts")
vocab = self._vocab_from_new(sentences)
# assign a unique index to each word
self.vocab, self.index2word = {}, []
for meta_word in [self.label0_as_vocab, self.label1_as_vocab, self.unknown_as_vocab]:
v = Vocab(count=1)
v.index = len(self.vocab)
v.sample_probability = 1.0
self.index2word.append(meta_word)
self.vocab[meta_word] = v
# remove word with count < min_count, default min_count = 5 in gensim, Seger changed to 1
# actually, not remove any words
# build self.vocab word->Vocab dict, and assign a unique index to each word
for subgram, v in iteritems(vocab):
if v.count >= self.min_count:
v.sample_probability = 1.0
v.index = len(self.vocab)
self.index2word.append(subgram)
self.vocab[subgram] = v
logger.info("total %i word types after removing those with count<%s" % (len(self.vocab), self.min_count))
logger.info('reset weights')
if self.hybrid_pred:
# v is word
# get single character word frequency
freq_list = [self.vocab[v].count for v in self.vocab if len(v) == 1]
freq_list.sort(reverse=True)
self.hybrid_threshold = freq_list[len(freq_list) / 25]
print '>frequencey threshold for hybrid prediction is:', self.hybrid_threshold
self.reset_weights()
def add_word_to_vocab(self, word, count=1):
v = Vocab(count=count)
v.index = len(self.vocab)
self.vocab[word] = v
self.index2word.append(word)
return v