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Added phrase support #135
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Added phrase support #135
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Original file line number | Diff line number | Diff line change |
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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. | ||
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**Install Cython with `pip install cython` before to use optimized word2vec training** (70x speedup [2]_). | ||
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Initialize a model with e.g.:: | ||
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>>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4) | ||
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@@ -44,7 +42,7 @@ | |
and so on. | ||
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.. [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/ | ||
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""" | ||
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import logging | ||
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@@ -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 | ||
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logger = logging.getLogger("gensim.models.word2vec") | ||
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logger = logging.getLogger("gensim.models.word2vec") | ||
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from gensim import utils, matutils # utility fnc for pickling, common scipy operations etc | ||
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@@ -131,7 +129,7 @@ class Word2Vec(utils.SaveLoad): | |
compatible with the original word2vec implementation via `save_word2vec_format()` and `load_word2vec_format()`. | ||
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""" | ||
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. | ||
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@@ -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 ). | ||
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""" | ||
self.vocab = {} # mapping from a word (string) to a Vocab object | ||
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@@ -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 | ||
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if sentences is not None: | ||
if phrase_pass > 0 : | ||
sentences = self.build_phrases( sentences) | ||
self.build_vocab(sentences) | ||
self.train(sentences) | ||
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def build_phrases( self, sentences ): | ||
""" | ||
Generate phrases for given sentences. | ||
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""" | ||
logger.info("building phrases. will run %i passes" % (self.phrase_pass) ) | ||
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for p in range(self.phrase_pass): | ||
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# build vocab | ||
logger.info("Start building phrases using sentences. Pass %i" % p) | ||
self.build_phrase_vocab( sentences ) | ||
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## lets train for phrases | ||
new_sentences = self.find_bigrams(sentences, float(self.thresholds[p]) ) | ||
sentences = new_sentences; | ||
return sentences | ||
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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. | ||
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""" | ||
logger.info("Selecting phrases based on threshold %i." % threshold ) | ||
new_sentences = [] | ||
total_bigrams = 0 | ||
for sentence_no, sentence in enumerate( sentences ): | ||
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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)) | ||
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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 ) | ||
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last_word = word | ||
last_word_count = word_count | ||
new_sentences.append( new_sentence ) | ||
logger.info("collected %i bigrams for %i sentences" % ( total_bigrams, (sentence_no+1) )) | ||
return new_sentences | ||
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def create_binary_tree(self): | ||
""" | ||
Create a binary Huffman tree using stored vocabulary word counts. Frequent words | ||
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@@ -194,6 +269,51 @@ def create_binary_tree(self): | |
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logger.info("built huffman tree with maximum node depth %i" % max_depth) | ||
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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. | ||
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""" | ||
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) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 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). |
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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)) | ||
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def build_vocab(self, sentences): | ||
""" | ||
Build vocabulary from a sequence of sentences (can be a once-only generator stream). | ||
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@@ -216,6 +336,14 @@ def build_vocab(self, sentences): | |
(len(vocab), total_words(), sentence_no + 1)) | ||
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# 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() | ||
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def create_unique_index( self, vocab ) : | ||
self.vocab, self.index2word = {}, [] | ||
for word, v in vocab.iteritems(): | ||
if v.count >= self.min_count: | ||
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@@ -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)) | ||
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# add info about each word's Huffman encoding | ||
self.create_binary_tree() | ||
self.reset_weights() | ||
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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. | ||
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""" | ||
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)) | ||
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if not self.vocab: | ||
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@@ -588,33 +709,13 @@ def __iter__(self): | |
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class LineSentence(object): | ||
def __init__(self, source): | ||
"""Simple format: one sentence = one line; words already preprocessed and separated by whitespace. | ||
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source can be either a string or a file object | ||
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Thus, one can use this for just plain files: | ||
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sentences = LineSentence('myfile.txt') | ||
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Or for compressed files: | ||
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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 | ||
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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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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).