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Added phrase support #135

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@@ -10,8 +10,6 @@
The training algorithm was originally ported from the C package https://code.google.com/p/word2vec/
and extended with additional functionality.
**Install Cython with `pip install cython` before to use optimized word2vec training** (70x speedup [2]_).
Initialize a model with e.g.::
>>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)
@@ -44,7 +42,7 @@
and so on.
.. [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.
.. [2] Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/
"""

import logging
@@ -60,8 +58,8 @@
from numpy import zeros_like, empty, exp, dot, outer, random, dtype, get_include,\
float32 as REAL, uint32, seterr, array, uint8, vstack, argsort, fromstring

logger = logging.getLogger("gensim.models.word2vec")

logger = logging.getLogger("gensim.models.word2vec")

from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc

@@ -131,7 +129,7 @@ class Word2Vec(utils.SaveLoad):
compatible with the original word2vec implementation via `save_word2vec_format()` and `load_word2vec_format()`.
"""
def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5, seed=1, workers=1, min_alpha=0.0001):
def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5, seed=1, workers=1, min_alpha=0.0001, thresholds= [100], phrase_pass=0):
"""
Initialize the model from an iterable of `sentences`. Each sentence is a
list of words (utf8 strings) that will be used for training.
@@ -146,6 +144,8 @@ def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5,
`seed` = for the random number generator.
`min_count` = ignore all words with total frequency lower than this.
`workers` = use this many worker threads to train the model (=faster training with multicore machines)
`phrase_pass` is number of times sentences are evaluated for phrase ( will result in higher order ngrams ).
`threshold` array of thresholds for forming the phrases at each pass. ( higher means less phrases ).
"""
self.vocab = {} # mapping from a word (string) to a Vocab object
@@ -157,11 +157,86 @@ def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5,
self.min_count = min_count
self.workers = workers
self.min_alpha = min_alpha
self.phrase_pass = int(phrase_pass)
self.thresholds = thresholds

if sentences is not None:
if phrase_pass > 0 :
sentences = self.build_phrases( sentences)
self.build_vocab(sentences)
self.train(sentences)


def build_phrases( self, sentences ):
"""
Generate phrases for given sentences.
"""
logger.info("building phrases. will run %i passes" % (self.phrase_pass) )

for p in range(self.phrase_pass):

# build vocab
logger.info("Start building phrases using sentences. Pass %i" % p)
self.build_phrase_vocab( sentences )

## lets train for phrases
new_sentences = self.find_bigrams(sentences, float(self.thresholds[p]) )
sentences = new_sentences;
return sentences


def find_bigrams( self, sentences, threshold ) :
"""
Find bigrams using words that appear together frequently together and infrequently
in other contexts. Frequent bigrams are calculated based on unigram and bigram counts.
"""
logger.info("Selecting phrases based on threshold %i." % threshold )
new_sentences = []
total_bigrams = 0
for sentence_no, sentence in enumerate( sentences ):

last_word, bigram_word = None, None
last_word_count, score, bigram_count = 0,0,0
new_sentence = []
for word in sentence:
out_of_vocab = False
if word in self.vocab:
word_count = self.vocab[word].count
else:
out_of_vocab = True
if last_word is None:
out_of_vocab = True
else :
bigram_word = last_word + '_' + word
if bigram_word in self.vocab:
bigram_count = self.vocab[bigram_word].count
else:
out_of_vocab = True
if ( last_word_count < self.min_count or word_count < self.min_count ):
out_of_vocab = True
if ( out_of_vocab ) :
score = 0
else :
score = ( bigram_count - self.min_count ) / ( float(last_word_count) * (float(word_count) / self.train_words))

if ( score > threshold ) :
## remove last word from the sentence and add the new bigram word
new_sentence.pop()
new_sentence.append( bigram_word )
total_bigrams +=1
word_count = 0
else:
new_sentence.append( word )

last_word = word
last_word_count = word_count
new_sentences.append( new_sentence )

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@piskvorky

piskvorky Nov 10, 2013

Member

this is not going to work -- you are creating the entire corpus as a list in memory! that wouldn't scale very well :)

the bigram sentences must be coming out as a stream (one sentence at a time).

logger.info("collected %i bigrams for %i sentences" % ( total_bigrams, (sentence_no+1) ))
return new_sentences


def create_binary_tree(self):
"""
Create a binary Huffman tree using stored vocabulary word counts. Frequent words
@@ -194,6 +269,51 @@ def create_binary_tree(self):

logger.info("built huffman tree with maximum node depth %i" % max_depth)


def build_phrase_vocab(self, sentences ):
"""
Build vocabulary from a sequence of sentences. Add all possible bi-grams to the dictionay.
Each sentence must be a list of utf8 strings.
"""
logger.info("collecting all words and their counts")
sentence_no, vocab = -1, {}
total_words = lambda: sum(v.count for v in vocab.itervalues())
train_words,bigram_total = 0, 0
for sentence_no, sentence in enumerate(sentences):
if sentence_no % 10000 == 0:
logger.info("PROGRESS: at sentence #%i, processed %i words and %i word types" %
(sentence_no, total_words(), len(vocab)))
last_word = None
for word in sentence:
train_words += 1
if word in vocab:
vocab[word].count += 1
else:
vocab[word] = Vocab(count=1)
## start of the sentence
if last_word is None:
last_word = word
continue
else:
bigram_word = last_word+'_'+word
last_word = word
if bigram_word in vocab:
vocab[bigram_word].count +=1
else:
vocab[bigram_word] = Vocab(count=1)

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@piskvorky

piskvorky Nov 10, 2013

Member

this won't scale -- there are too many word combinations, cannot store them all in memory.

the original C code solves this by pruning infrequent entries from memory from time to time (a hack).

bigram_total +=1
logger.info("collected %i bigrams from a corpus of %i words and %i sentences" %
(bigram_total, total_words(), sentence_no+1 ))
logger.info("collected %i word types from a corpus of %i words and %i sentences" %
(len(vocab), total_words(), sentence_no + 1))
self.train_words = train_words
# assign a unique index to each word
self.create_unique_index( vocab )
logger.info("total %i word types after removing those with count<%s" % (len(self.vocab), self.min_count))



def build_vocab(self, sentences):
"""
Build vocabulary from a sequence of sentences (can be a once-only generator stream).
@@ -216,6 +336,14 @@ def build_vocab(self, sentences):
(len(vocab), total_words(), sentence_no + 1))

# assign a unique index to each word
self.create_unique_index( vocab )
# add info about each word's Huffman encoding
self.create_binary_tree()
self.reset_weights()



def create_unique_index( self, vocab ) :
self.vocab, self.index2word = {}, []
for word, v in vocab.iteritems():
if v.count >= self.min_count:
@@ -224,20 +352,13 @@ def build_vocab(self, sentences):
self.vocab[word] = v
logger.info("total %i word types after removing those with count<%s" % (len(self.vocab), self.min_count))

# add info about each word's Huffman encoding
self.create_binary_tree()
self.reset_weights()


def train(self, sentences, total_words=None, word_count=0, chunksize=100):
"""
Update the model's neural weights from a sequence of sentences (can be a once-only generator stream).
Each sentence must be a list of utf8 strings.
"""
if FAST_VERSION < 0:
import warnings
warnings.warn("Cython compilation failed, training will be slow. Do you have Cython installed? `pip install cython`")
logger.info("training model with %i workers on %i vocabulary and %i features" % (self.workers, len(self.vocab), self.layer1_size))

if not self.vocab:
@@ -588,33 +709,13 @@ def __iter__(self):


class LineSentence(object):
def __init__(self, source):
"""Simple format: one sentence = one line; words already preprocessed and separated by whitespace.
source can be either a string or a file object
Thus, one can use this for just plain files:
sentences = LineSentence('myfile.txt')
Or for compressed files:
sentences = LineSentence(bz2.BZ2File('compressed_text.bz2'))
"""
self.source = source
def __init__(self, fname):
"""Simple format: one sentence = one line; words already preprocessed and separated by whitespace."""
self.fname = fname

def __iter__(self):
"""Iterate through the lines in the source."""
try:
# Assume it is a file-like object and try treating it as such
# Things that don't have seek will trigger an exception
self.source.seek(0)
for line in self.source:
yield line.split()
except AttributeError:
# If it didn't work like a file, use it as a string filename
for line in open(self.source):
yield line.split()
for line in open(self.fname):
yield line.split()



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