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laywerrobot/lib/python3.6/site-packages/gensim/models/deprecated/word2vec.py

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2013 Radim Rehurek <me@radimrehurek.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Warnings
--------
.. deprecated:: 3.3.0
Use :mod:`gensim.models.word2vec` instead.
Produce word vectors with deep learning via word2vec's "skip-gram and CBOW models", using either
hierarchical softmax or negative sampling [1]_ [2]_.
NOTE: There are more ways to get word vectors in Gensim than just Word2Vec.
See wrappers for FastText, VarEmbed and WordRank.
The training algorithms were originally ported from the C package https://code.google.com/p/word2vec/
and extended with additional functionality.
For a blog tutorial on gensim word2vec, with an interactive web app trained on GoogleNews,
visit http://radimrehurek.com/2014/02/word2vec-tutorial/
**Make sure you have a C compiler before installing gensim, to use optimized (compiled) word2vec training**
(70x speedup compared to plain NumPy implementation [3]_).
Initialize a model with e.g.::
>>> model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)
Persist a model to disk with::
>>> model.save(fname)
>>> model = Word2Vec.load(fname) # you can continue training with the loaded model!
The word vectors are stored in a KeyedVectors instance in model.wv.
This separates the read-only word vector lookup operations in KeyedVectors from the training code in Word2Vec::
>>> model.wv['computer'] # numpy vector of a word
array([-0.00449447, -0.00310097, 0.02421786, ...], dtype=float32)
The word vectors can also be instantiated from an existing file on disk in the word2vec C format
as a KeyedVectors instance::
NOTE: It is impossible to continue training the vectors loaded from the C format because hidden weights,
vocabulary frequency and the binary tree is missing::
>>> from gensim.models.keyedvectors import KeyedVectors
>>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.txt', binary=False) # C text format
>>> word_vectors = KeyedVectors.load_word2vec_format('/tmp/vectors.bin', binary=True) # C binary format
You can perform various NLP word tasks with the model. Some of them
are already built-in::
>>> model.wv.most_similar(positive=['woman', 'king'], negative=['man'])
[('queen', 0.50882536), ...]
>>> model.wv.most_similar_cosmul(positive=['woman', 'king'], negative=['man'])
[('queen', 0.71382287), ...]
>>> model.wv.doesnt_match("breakfast cereal dinner lunch".split())
'cereal'
>>> model.wv.similarity('woman', 'man')
0.73723527
Probability of a text under the model::
>>> model.score(["The fox jumped over a lazy dog".split()])
0.2158356
Correlation with human opinion on word similarity::
>>> model.wv.evaluate_word_pairs(os.path.join(module_path, 'test_data','wordsim353.tsv'))
0.51, 0.62, 0.13
And on analogies::
>>> model.wv.accuracy(os.path.join(module_path, 'test_data', 'questions-words.txt'))
and so on.
If you're finished training a model (i.e. no more updates, only querying),
then switch to the :mod:`gensim.models.KeyedVectors` instance in wv
>>> word_vectors = model.wv
>>> del model
to trim unneeded model memory = use much less RAM.
Note that there is a :mod:`gensim.models.phrases` module which lets you automatically
detect phrases longer than one word. Using phrases, you can learn a word2vec model
where "words" are actually multiword expressions, such as `new_york_times` or `financial_crisis`:
>>> bigram_transformer = gensim.models.Phrases(sentences)
>>> model = Word2Vec(bigram_transformer[sentences], size=100, ...)
.. [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] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean.
Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.
.. [3] Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/
"""
from __future__ import division # py3 "true division"
import logging
import sys
import os
import heapq
from timeit import default_timer
from copy import deepcopy
from collections import defaultdict
import threading
import itertools
import warnings
from gensim.utils import keep_vocab_item, call_on_class_only
from gensim.models.deprecated.keyedvectors import KeyedVectors, Vocab
from gensim.models.word2vec import Word2Vec as NewWord2Vec
from gensim.models.deprecated.old_saveload import SaveLoad
try:
from queue import Queue, Empty
except ImportError:
from Queue import Queue, Empty
from numpy import exp, log, dot, zeros, outer, random, dtype, float32 as REAL,\
uint32, seterr, array, uint8, vstack, fromstring, sqrt,\
empty, sum as np_sum, ones, logaddexp
from scipy.special import expit
from gensim import utils
from gensim import matutils # utility fnc for pickling, common scipy operations etc
from six import iteritems, itervalues, string_types
from six.moves import xrange
from types import GeneratorType
logger = logging.getLogger(__name__)
# failed... fall back to plain numpy (20-80x slower training than the above)
FAST_VERSION = -1
MAX_WORDS_IN_BATCH = 10000
def load_old_word2vec(*args, **kwargs):
old_model = Word2Vec.load(*args, **kwargs)
vector_size = getattr(old_model, 'vector_size', old_model.layer1_size)
params = {
'size': vector_size,
'alpha': old_model.alpha,
'window': old_model.window,
'min_count': old_model.min_count,
'max_vocab_size': old_model.__dict__.get('max_vocab_size', None),
'sample': old_model.__dict__.get('sample', 1e-3),
'seed': old_model.seed,
'workers': old_model.workers,
'min_alpha': old_model.min_alpha,
'sg': old_model.sg,
'hs': old_model.hs,
'negative': old_model.negative,
'cbow_mean': old_model.cbow_mean,
'hashfxn': old_model.__dict__.get('hashfxn', hash),
'iter': old_model.__dict__.get('iter', 5),
'null_word': old_model.__dict__.get('null_word', 0),
'sorted_vocab': old_model.__dict__.get('sorted_vocab', 1),
'batch_words': old_model.__dict__.get('batch_words', MAX_WORDS_IN_BATCH),
'compute_loss': old_model.__dict__.get('compute_loss', None)
}
new_model = NewWord2Vec(**params)
# set trainables attributes
new_model.wv.vectors = old_model.wv.syn0
if hasattr(old_model.wv, 'syn0norm'):
new_model.wv.vectors_norm = old_model.wv.syn0norm
if hasattr(old_model, 'syn1'):
new_model.trainables.syn1 = old_model.syn1
if hasattr(old_model, 'syn1neg'):
new_model.trainables.syn1neg = old_model.syn1neg
if hasattr(old_model, 'syn0_lockf'):
new_model.trainables.vectors_lockf = old_model.syn0_lockf
# set vocabulary attributes
new_model.wv.vocab = old_model.wv.vocab
new_model.wv.index2word = old_model.wv.index2word
new_model.vocabulary.cum_table = old_model.__dict__.get('cum_table', None)
new_model.train_count = old_model.__dict__.get('train_count', None)
new_model.corpus_count = old_model.__dict__.get('corpus_count', None)
new_model.running_training_loss = old_model.__dict__.get('running_training_loss', 0)
new_model.total_train_time = old_model.__dict__.get('total_train_time', None)
new_model.min_alpha_yet_reached = old_model.__dict__.get('min_alpha_yet_reached', old_model.alpha)
new_model.model_trimmed_post_training = old_model.__dict__.get('model_trimmed_post_training', None)
return new_model
def train_batch_sg(model, sentences, alpha, work=None, compute_loss=False):
"""
Update skip-gram model by training on a sequence of sentences.
Each sentence is a list of string tokens, which are looked up in the model's
vocab dictionary. Called internally from `Word2Vec.train()`.
This is the non-optimized, Python version. If you have cython installed, gensim
will use the optimized version from word2vec_inner instead.
"""
result = 0
for sentence in sentences:
word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab and
model.wv.vocab[w].sample_int > model.random.rand() * 2**32]
for pos, word in enumerate(word_vocabs):
reduced_window = model.random.randint(model.window) # `b` in the original word2vec code
# now go over all words from the (reduced) window, predicting each one in turn
start = max(0, pos - model.window + reduced_window)
for pos2, word2 in enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start):
# don't train on the `word` itself
if pos2 != pos:
train_sg_pair(
model, model.wv.index2word[word.index], word2.index, alpha, compute_loss=compute_loss
)
result += len(word_vocabs)
return result
def train_batch_cbow(model, sentences, alpha, work=None, neu1=None, compute_loss=False):
"""
Update CBOW model by training on a sequence of sentences.
Each sentence is a list of string tokens, which are looked up in the model's
vocab dictionary. Called internally from `Word2Vec.train()`.
This is the non-optimized, Python version. If you have cython installed, gensim
will use the optimized version from word2vec_inner instead.
"""
result = 0
for sentence in sentences:
word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab and
model.wv.vocab[w].sample_int > model.random.rand() * 2**32]
for pos, word in enumerate(word_vocabs):
reduced_window = model.random.randint(model.window) # `b` in the original word2vec code
start = max(0, pos - model.window + reduced_window)
window_pos = enumerate(word_vocabs[start:(pos + model.window + 1 - reduced_window)], start)
word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)]
l1 = np_sum(model.wv.syn0[word2_indices], axis=0) # 1 x vector_size
if word2_indices and model.cbow_mean:
l1 /= len(word2_indices)
train_cbow_pair(model, word, word2_indices, l1, alpha, compute_loss=compute_loss)
result += len(word_vocabs)
return result
def score_sentence_sg(model, sentence, work=None):
"""
Obtain likelihood score for a single sentence in a fitted skip-gram representaion.
The sentence is a list of Vocab objects (or None, when the corresponding
word is not in the vocabulary). Called internally from `Word2Vec.score()`.
This is the non-optimized, Python version. If you have cython installed, gensim
will use the optimized version from word2vec_inner instead.
"""
log_prob_sentence = 0.0
if model.negative:
raise RuntimeError("scoring is only available for HS=True")
word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab]
for pos, word in enumerate(word_vocabs):
if word is None:
continue # OOV word in the input sentence => skip
# now go over all words from the window, predicting each one in turn
start = max(0, pos - model.window)
for pos2, word2 in enumerate(word_vocabs[start: pos + model.window + 1], start):
# don't train on OOV words and on the `word` itself
if word2 is not None and pos2 != pos:
log_prob_sentence += score_sg_pair(model, word, word2)
return log_prob_sentence
def score_sentence_cbow(model, sentence, work=None, neu1=None):
"""
Obtain likelihood score for a single sentence in a fitted CBOW representaion.
The sentence is a list of Vocab objects (or None, where the corresponding
word is not in the vocabulary. Called internally from `Word2Vec.score()`.
This is the non-optimized, Python version. If you have cython installed, gensim
will use the optimized version from word2vec_inner instead.
"""
log_prob_sentence = 0.0
if model.negative:
raise RuntimeError("scoring is only available for HS=True")
word_vocabs = [model.wv.vocab[w] for w in sentence if w in model.wv.vocab]
for pos, word in enumerate(word_vocabs):
if word is None:
continue # OOV word in the input sentence => skip
start = max(0, pos - model.window)
window_pos = enumerate(word_vocabs[start:(pos + model.window + 1)], start)
word2_indices = [word2.index for pos2, word2 in window_pos if (word2 is not None and pos2 != pos)]
l1 = np_sum(model.wv.syn0[word2_indices], axis=0) # 1 x layer1_size
if word2_indices and model.cbow_mean:
l1 /= len(word2_indices)
log_prob_sentence += score_cbow_pair(model, word, l1)
return log_prob_sentence
def train_sg_pair(model, word, context_index, alpha, learn_vectors=True, learn_hidden=True,
context_vectors=None, context_locks=None, compute_loss=False, is_ft=False):
if context_vectors is None:
if is_ft:
context_vectors_vocab = model.wv.syn0_vocab
context_vectors_ngrams = model.wv.syn0_ngrams
else:
context_vectors = model.wv.syn0
if context_locks is None:
if is_ft:
context_locks_vocab = model.syn0_vocab_lockf
context_locks_ngrams = model.syn0_ngrams_lockf
else:
context_locks = model.syn0_lockf
if word not in model.wv.vocab:
return
predict_word = model.wv.vocab[word] # target word (NN output)
if is_ft:
l1_vocab = context_vectors_vocab[context_index[0]]
l1_ngrams = np_sum(context_vectors_ngrams[context_index[1:]], axis=0)
if context_index:
l1 = np_sum([l1_vocab, l1_ngrams], axis=0) / len(context_index)
else:
l1 = context_vectors[context_index] # input word (NN input/projection layer)
lock_factor = context_locks[context_index]
neu1e = zeros(l1.shape)
if model.hs:
# work on the entire tree at once, to push as much work into numpy's C routines as possible (performance)
l2a = deepcopy(model.syn1[predict_word.point]) # 2d matrix, codelen x layer1_size
prod_term = dot(l1, l2a.T)
fa = expit(prod_term) # propagate hidden -> output
ga = (1 - predict_word.code - fa) * alpha # vector of error gradients multiplied by the learning rate
if learn_hidden:
model.syn1[predict_word.point] += outer(ga, l1) # learn hidden -> output
neu1e += dot(ga, l2a) # save error
# loss component corresponding to hierarchical softmax
if compute_loss:
sgn = (-1.0)**predict_word.code # `ch` function, 0 -> 1, 1 -> -1
lprob = -log(expit(-sgn * prod_term))
model.running_training_loss += sum(lprob)
if model.negative:
# use this word (label = 1) + `negative` other random words not from this sentence (label = 0)
word_indices = [predict_word.index]
while len(word_indices) < model.negative + 1:
w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1]))
if w != predict_word.index:
word_indices.append(w)
l2b = model.syn1neg[word_indices] # 2d matrix, k+1 x layer1_size
prod_term = dot(l1, l2b.T)
fb = expit(prod_term) # propagate hidden -> output
gb = (model.neg_labels - fb) * alpha # vector of error gradients multiplied by the learning rate
if learn_hidden:
model.syn1neg[word_indices] += outer(gb, l1) # learn hidden -> output
neu1e += dot(gb, l2b) # save error
# loss component corresponding to negative sampling
if compute_loss:
model.running_training_loss -= sum(log(expit(-1 * prod_term[1:]))) # for the sampled words
model.running_training_loss -= log(expit(prod_term[0])) # for the output word
if learn_vectors:
if is_ft:
model.wv.syn0_vocab[context_index[0]] += neu1e * context_locks_vocab[context_index[0]]
for i in context_index[1:]:
model.wv.syn0_ngrams[i] += neu1e * context_locks_ngrams[i]
else:
l1 += neu1e * lock_factor # learn input -> hidden (mutates model.wv.syn0[word2.index], if that is l1)
return neu1e
def train_cbow_pair(model, word, input_word_indices, l1, alpha, learn_vectors=True, learn_hidden=True,
compute_loss=False, context_vectors=None, context_locks=None, is_ft=False):
if context_vectors is None:
if is_ft:
context_vectors_vocab = model.wv.syn0_vocab
context_vectors_ngrams = model.wv.syn0_ngrams
else:
context_vectors = model.wv.syn0
if context_locks is None:
if is_ft:
context_locks_vocab = model.syn0_vocab_lockf
context_locks_ngrams = model.syn0_ngrams_lockf
else:
context_locks = model.syn0_lockf
neu1e = zeros(l1.shape)
if model.hs:
l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size
prod_term = dot(l1, l2a.T)
fa = expit(prod_term) # propagate hidden -> output
ga = (1. - word.code - fa) * alpha # vector of error gradients multiplied by the learning rate
if learn_hidden:
model.syn1[word.point] += outer(ga, l1) # learn hidden -> output
neu1e += dot(ga, l2a) # save error
# loss component corresponding to hierarchical softmax
if compute_loss:
sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1
model.running_training_loss += sum(-log(expit(-sgn * prod_term)))
if model.negative:
# use this word (label = 1) + `negative` other random words not from this sentence (label = 0)
word_indices = [word.index]
while len(word_indices) < model.negative + 1:
w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1]))
if w != word.index:
word_indices.append(w)
l2b = model.syn1neg[word_indices] # 2d matrix, k+1 x layer1_size
prod_term = dot(l1, l2b.T)
fb = expit(prod_term) # propagate hidden -> output
gb = (model.neg_labels - fb) * alpha # vector of error gradients multiplied by the learning rate
if learn_hidden:
model.syn1neg[word_indices] += outer(gb, l1) # learn hidden -> output
neu1e += dot(gb, l2b) # save error
# loss component corresponding to negative sampling
if compute_loss:
model.running_training_loss -= sum(log(expit(-1 * prod_term[1:]))) # for the sampled words
model.running_training_loss -= log(expit(prod_term[0])) # for the output word
if learn_vectors:
# learn input -> hidden, here for all words in the window separately
if is_ft:
if not model.cbow_mean and input_word_indices:
neu1e /= (len(input_word_indices[0]) + len(input_word_indices[1]))
for i in input_word_indices[0]:
context_vectors_vocab[i] += neu1e * context_locks_vocab[i]
for i in input_word_indices[1]:
context_vectors_ngrams[i] += neu1e * context_locks_ngrams[i]
else:
if not model.cbow_mean and input_word_indices:
neu1e /= len(input_word_indices)
for i in input_word_indices:
context_vectors[i] += neu1e * context_locks[i]
return neu1e
def score_sg_pair(model, word, word2):
l1 = model.wv.syn0[word2.index]
l2a = deepcopy(model.syn1[word.point]) # 2d matrix, codelen x layer1_size
sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1
lprob = -logaddexp(0, -sgn * dot(l1, l2a.T))
return sum(lprob)
def score_cbow_pair(model, word, l1):
l2a = model.syn1[word.point] # 2d matrix, codelen x layer1_size
sgn = (-1.0)**word.code # ch function, 0-> 1, 1 -> -1
lprob = -logaddexp(0, -sgn * dot(l1, l2a.T))
return sum(lprob)
class Word2Vec(SaveLoad):
"""
Class for training, using and evaluating neural networks described in https://code.google.com/p/word2vec/
If you're finished training a model (=no more updates, only querying)
then switch to the :mod:`gensim.models.KeyedVectors` instance in wv
The model can be stored/loaded via its `save()` and `load()` methods, or stored/loaded in a format
compatible with the original word2vec implementation via `wv.save_word2vec_format()`
and `KeyedVectors.load_word2vec_format()`.
"""
def __init__(self, sentences=None, size=100, alpha=0.025, window=5, min_count=5,
max_vocab_size=None, sample=1e-3, seed=1, workers=3, min_alpha=0.0001,
sg=0, hs=0, negative=5, cbow_mean=1, hashfxn=hash, iter=5, null_word=0,
trim_rule=None, sorted_vocab=1, batch_words=MAX_WORDS_IN_BATCH, compute_loss=False):
"""
Initialize the model from an iterable of `sentences`. Each sentence is a
list of words (unicode strings) that will be used for training.
The `sentences` iterable can be simply a list, but for larger corpora,
consider an iterable that streams the sentences directly from disk/network.
See :class:`BrownCorpus`, :class:`Text8Corpus` or :class:`LineSentence` in
this module for such examples.
If you don't supply `sentences`, the model is left uninitialized -- use if
you plan to initialize it in some other way.
`sg` defines the training algorithm. By default (`sg=0`), CBOW is used.
Otherwise (`sg=1`), skip-gram is employed.
`size` is the dimensionality of the feature vectors.
`window` is the maximum distance between the current and predicted word within a sentence.
`alpha` is the initial learning rate (will linearly drop to `min_alpha` as training progresses).
`seed` = for the random number generator. Initial vectors for each
word are seeded with a hash of the concatenation of word + str(seed).
Note that for a fully deterministically-reproducible run, you must also limit the model to
a single worker thread, to eliminate ordering jitter from OS thread scheduling. (In Python
3, reproducibility between interpreter launches also requires use of the PYTHONHASHSEED
environment variable to control hash randomization.)
`min_count` = ignore all words with total frequency lower than this.
`max_vocab_size` = limit RAM during vocabulary building; if there are more unique
words than this, then prune the infrequent ones. Every 10 million word types
need about 1GB of RAM. Set to `None` for no limit (default).
`sample` = threshold for configuring which higher-frequency words are randomly downsampled;
default is 1e-3, useful range is (0, 1e-5).
`workers` = use this many worker threads to train the model (=faster training with multicore machines).
`hs` = if 1, hierarchical softmax will be used for model training.
If set to 0 (default), and `negative` is non-zero, negative sampling will be used.
`negative` = if > 0, negative sampling will be used, the int for negative
specifies how many "noise words" should be drawn (usually between 5-20).
Default is 5. If set to 0, no negative samping is used.
`cbow_mean` = if 0, use the sum of the context word vectors. If 1 (default), use the mean.
Only applies when cbow is used.
`hashfxn` = hash function to use to randomly initialize weights, for increased
training reproducibility. Default is Python's rudimentary built in hash function.
`iter` = number of iterations (epochs) over the corpus. Default is 5.
`trim_rule` = vocabulary trimming rule, specifies whether certain words should remain
in the vocabulary, be trimmed away, or handled using the default (discard if word count < min_count).
Can be None (min_count will be used), or a callable that accepts parameters (word, count, min_count) and
returns either `utils.RULE_DISCARD`, `utils.RULE_KEEP` or `utils.RULE_DEFAULT`.
Note: The rule, if given, is only used to prune vocabulary during build_vocab() and is not stored as part
of the model.
`sorted_vocab` = if 1 (default), sort the vocabulary by descending frequency before
assigning word indexes.
`batch_words` = target size (in words) for batches of examples passed to worker threads (and
thus cython routines). Default is 10000. (Larger batches will be passed if individual
texts are longer than 10000 words, but the standard cython code truncates to that maximum.)
"""
self.load = call_on_class_only
if FAST_VERSION == -1:
logger.warning('Slow version of %s is being used', __name__)
else:
logger.debug('Fast version of %s is being used', __name__)
self.initialize_word_vectors()
self.sg = int(sg)
self.cum_table = None # for negative sampling
self.vector_size = int(size)
self.layer1_size = int(size)
if size % 4 != 0:
logger.warning("consider setting layer size to a multiple of 4 for greater performance")
self.alpha = float(alpha)
self.min_alpha_yet_reached = float(alpha) # To warn user if alpha increases
self.window = int(window)
self.max_vocab_size = max_vocab_size
self.seed = seed
self.random = random.RandomState(seed)
self.min_count = min_count
self.sample = sample
self.workers = int(workers)
self.min_alpha = float(min_alpha)
self.hs = hs
self.negative = negative
self.cbow_mean = int(cbow_mean)
self.hashfxn = hashfxn
self.iter = iter
self.null_word = null_word
self.train_count = 0
self.total_train_time = 0
self.sorted_vocab = sorted_vocab
self.batch_words = batch_words
self.model_trimmed_post_training = False
self.compute_loss = compute_loss
self.running_training_loss = 0
if sentences is not None:
if isinstance(sentences, GeneratorType):
raise TypeError("You can't pass a generator as the sentences argument. Try an iterator.")
self.build_vocab(sentences, trim_rule=trim_rule)
self.train(
sentences, total_examples=self.corpus_count, epochs=self.iter,
start_alpha=self.alpha, end_alpha=self.min_alpha
)
else:
if trim_rule is not None:
logger.warning(
"The rule, if given, is only used to prune vocabulary during build_vocab() "
"and is not stored as part of the model. Model initialized without sentences. "
"trim_rule provided, if any, will be ignored."
)
def initialize_word_vectors(self):
self.wv = KeyedVectors()
def make_cum_table(self, power=0.75, domain=2**31 - 1):
"""
Create a cumulative-distribution table using stored vocabulary word counts for
drawing random words in the negative-sampling training routines.
To draw a word index, choose a random integer up to the maximum value in the
table (cum_table[-1]), then finding that integer's sorted insertion point
(as if by bisect_left or ndarray.searchsorted()). That insertion point is the
drawn index, coming up in proportion equal to the increment at that slot.
Called internally from 'build_vocab()'.
"""
vocab_size = len(self.wv.index2word)
self.cum_table = zeros(vocab_size, dtype=uint32)
# compute sum of all power (Z in paper)
train_words_pow = 0.0
for word_index in xrange(vocab_size):
train_words_pow += self.wv.vocab[self.wv.index2word[word_index]].count**power
cumulative = 0.0
for word_index in xrange(vocab_size):
cumulative += self.wv.vocab[self.wv.index2word[word_index]].count**power
self.cum_table[word_index] = round(cumulative / train_words_pow * domain)
if len(self.cum_table) > 0:
assert self.cum_table[-1] == domain
def create_binary_tree(self):
"""
Create a binary Huffman tree using stored vocabulary word counts. Frequent words
will have shorter binary codes. Called internally from `build_vocab()`.
"""
logger.info("constructing a huffman tree from %i words", len(self.wv.vocab))
# build the huffman tree
heap = list(itervalues(self.wv.vocab))
heapq.heapify(heap)
for i in xrange(len(self.wv.vocab) - 1):
min1, min2 = heapq.heappop(heap), heapq.heappop(heap)
heapq.heappush(
heap, Vocab(count=min1.count + min2.count, index=i + len(self.wv.vocab), left=min1, right=min2)
)
# recurse over the tree, assigning a binary code to each vocabulary word
if heap:
max_depth, stack = 0, [(heap[0], [], [])]
while stack:
node, codes, points = stack.pop()
if node.index < len(self.wv.vocab):
# leaf node => store its path from the root
node.code, node.point = codes, points
max_depth = max(len(codes), max_depth)
else:
# inner node => continue recursion
points = array(list(points) + [node.index - len(self.wv.vocab)], dtype=uint32)
stack.append((node.left, array(list(codes) + [0], dtype=uint8), points))
stack.append((node.right, array(list(codes) + [1], dtype=uint8), points))
logger.info("built huffman tree with maximum node depth %i", max_depth)
def build_vocab(self, sentences, keep_raw_vocab=False, trim_rule=None, progress_per=10000, update=False):
"""
Build vocabulary from a sequence of sentences (can be a once-only generator stream).
Each sentence must be a list of unicode strings.
"""
self.scan_vocab(sentences, progress_per=progress_per, trim_rule=trim_rule) # initial survey
# trim by min_count & precalculate downsampling
self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update)
self.finalize_vocab(update=update) # build tables & arrays
def build_vocab_from_freq(self, word_freq, keep_raw_vocab=False, corpus_count=None, trim_rule=None, update=False):
"""
Build vocabulary from a dictionary of word frequencies.
Build model vocabulary from a passed dictionary that contains (word,word count).
Words must be of type unicode strings.
Parameters
----------
`word_freq` : dict
Word,Word_Count dictionary.
`keep_raw_vocab` : bool
If not true, delete the raw vocabulary after the scaling is done and free up RAM.
`corpus_count`: int
Even if no corpus is provided, this argument can set corpus_count explicitly.
`trim_rule` = vocabulary trimming rule, specifies whether certain words should remain
in the vocabulary, be trimmed away, or handled using the default (discard if word count < min_count).
Can be None (min_count will be used), or a callable that accepts parameters (word, count, min_count) and
returns either `utils.RULE_DISCARD`, `utils.RULE_KEEP` or `utils.RULE_DEFAULT`.
`update`: bool
If true, the new provided words in `word_freq` dict will be added to model's vocab.
Returns
--------
None
Examples
--------
>>> from gensim.models.word2vec import Word2Vec
>>> model= Word2Vec()
>>> model.build_vocab_from_freq({"Word1": 15, "Word2": 20})
"""
logger.info("Processing provided word frequencies")
# Instead of scanning text, this will assign provided word frequencies dictionary(word_freq)
# to be directly the raw vocab
raw_vocab = word_freq
logger.info(
"collected %i different raw word, with total frequency of %i",
len(raw_vocab), sum(itervalues(raw_vocab))
)
# Since no sentences are provided, this is to control the corpus_count
self.corpus_count = corpus_count if corpus_count else 0
self.raw_vocab = raw_vocab
# trim by min_count & precalculate downsampling
self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update)
self.finalize_vocab(update=update) # build tables & arrays
def scan_vocab(self, sentences, progress_per=10000, trim_rule=None):
"""Do an initial scan of all words appearing in sentences."""
logger.info("collecting all words and their counts")
sentence_no = -1
total_words = 0
min_reduce = 1
vocab = defaultdict(int)
checked_string_types = 0
for sentence_no, sentence in enumerate(sentences):
if not checked_string_types:
if isinstance(sentence, string_types):
logger.warning(
"Each 'sentences' item should be a list of words (usually unicode strings). "
"First item here is instead plain %s.",
type(sentence)
)
checked_string_types += 1
if sentence_no % progress_per == 0:
logger.info(
"PROGRESS: at sentence #%i, processed %i words, keeping %i word types",
sentence_no, total_words, len(vocab)
)
for word in sentence:
vocab[word] += 1
total_words += len(sentence)
if self.max_vocab_size and len(vocab) > self.max_vocab_size:
utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule)
min_reduce += 1
logger.info(
"collected %i word types from a corpus of %i raw words and %i sentences",
len(vocab), total_words, sentence_no + 1
)
self.corpus_count = sentence_no + 1
self.raw_vocab = vocab
return total_words
def scale_vocab(self, min_count=None, sample=None, dry_run=False,
keep_raw_vocab=False, trim_rule=None, update=False):
"""
Apply vocabulary settings for `min_count` (discarding less-frequent words)
and `sample` (controlling the downsampling of more-frequent words).
Calling with `dry_run=True` will only simulate the provided settings and
report the size of the retained vocabulary, effective corpus length, and
estimated memory requirements. Results are both printed via logging and
returned as a dict.
Delete the raw vocabulary after the scaling is done to free up RAM,
unless `keep_raw_vocab` is set.
"""
min_count = min_count or self.min_count
sample = sample or self.sample
drop_total = drop_unique = 0
if not update:
logger.info("Loading a fresh vocabulary")
retain_total, retain_words = 0, []
# Discard words less-frequent than min_count
if not dry_run:
self.wv.index2word = []
# make stored settings match these applied settings
self.min_count = min_count
self.sample = sample
self.wv.vocab = {}
for word, v in iteritems(self.raw_vocab):
if keep_vocab_item(word, v, min_count, trim_rule=trim_rule):
retain_words.append(word)
retain_total += v
if not dry_run:
self.wv.vocab[word] = Vocab(count=v, index=len(self.wv.index2word))
self.wv.index2word.append(word)
else:
drop_unique += 1
drop_total += v
original_unique_total = len(retain_words) + drop_unique
retain_unique_pct = len(retain_words) * 100 / max(original_unique_total, 1)
logger.info(
"min_count=%d retains %i unique words (%i%% of original %i, drops %i)",
min_count, len(retain_words), retain_unique_pct, original_unique_total, drop_unique
)
original_total = retain_total + drop_total
retain_pct = retain_total * 100 / max(original_total, 1)
logger.info(
"min_count=%d leaves %i word corpus (%i%% of original %i, drops %i)",
min_count, retain_total, retain_pct, original_total, drop_total
)
else:
logger.info("Updating model with new vocabulary")
new_total = pre_exist_total = 0
new_words = pre_exist_words = []
for word, v in iteritems(self.raw_vocab):
if keep_vocab_item(word, v, min_count, trim_rule=trim_rule):
if word in self.wv.vocab:
pre_exist_words.append(word)
pre_exist_total += v
if not dry_run:
self.wv.vocab[word].count += v
else:
new_words.append(word)
new_total += v
if not dry_run:
self.wv.vocab[word] = Vocab(count=v, index=len(self.wv.index2word))
self.wv.index2word.append(word)
else:
drop_unique += 1
drop_total += v
original_unique_total = len(pre_exist_words) + len(new_words) + drop_unique
pre_exist_unique_pct = len(pre_exist_words) * 100 / max(original_unique_total, 1)
new_unique_pct = len(new_words) * 100 / max(original_unique_total, 1)
logger.info(
"New added %i unique words (%i%% of original %i) "
"and increased the count of %i pre-existing words (%i%% of original %i)",
len(new_words), new_unique_pct, original_unique_total, len(pre_exist_words),
pre_exist_unique_pct, original_unique_total
)
retain_words = new_words + pre_exist_words
retain_total = new_total + pre_exist_total
# Precalculate each vocabulary item's threshold for sampling
if not sample:
# no words downsampled
threshold_count = retain_total
elif sample < 1.0:
# traditional meaning: set parameter as proportion of total
threshold_count = sample * retain_total
else:
# new shorthand: sample >= 1 means downsample all words with higher count than sample
threshold_count = int(sample * (3 + sqrt(5)) / 2)
downsample_total, downsample_unique = 0, 0
for w in retain_words:
v = self.raw_vocab[w]
word_probability = (sqrt(v / threshold_count) + 1) * (threshold_count / v)
if word_probability < 1.0:
downsample_unique += 1
downsample_total += word_probability * v
else:
word_probability = 1.0
downsample_total += v
if not dry_run:
self.wv.vocab[w].sample_int = int(round(word_probability * 2**32))
if not dry_run and not keep_raw_vocab:
logger.info("deleting the raw counts dictionary of %i items", len(self.raw_vocab))
self.raw_vocab = defaultdict(int)
logger.info("sample=%g downsamples %i most-common words", sample, downsample_unique)
logger.info(
"downsampling leaves estimated %i word corpus (%.1f%% of prior %i)",
downsample_total, downsample_total * 100.0 / max(retain_total, 1), retain_total
)
# return from each step: words-affected, resulting-corpus-size, extra memory estimates
report_values = {
'drop_unique': drop_unique, 'retain_total': retain_total, 'downsample_unique': downsample_unique,
'downsample_total': int(downsample_total), 'memory': self.estimate_memory(vocab_size=len(retain_words))
}
return report_values
def finalize_vocab(self, update=False):
"""Build tables and model weights based on final vocabulary settings."""
if not self.wv.index2word:
self.scale_vocab()
if self.sorted_vocab and not update:
self.sort_vocab()
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_cum_table()
if self.null_word:
# create null pseudo-word for padding when using concatenative L1 (run-of-words)
# this word is only ever input – never predicted – so count, huffman-point, etc doesn't matter
word, v = '\0', Vocab(count=1, sample_int=0)
v.index = len(self.wv.vocab)
self.wv.index2word.append(word)
self.wv.vocab[word] = v
# set initial input/projection and hidden weights
if not update:
self.reset_weights()
else:
self.update_weights()
def sort_vocab(self):
"""Sort the vocabulary so the most frequent words have the lowest indexes."""
if len(self.wv.syn0):
raise RuntimeError("cannot sort vocabulary after model weights already initialized.")
self.wv.index2word.sort(key=lambda word: self.wv.vocab[word].count, reverse=True)
for i, word in enumerate(self.wv.index2word):
self.wv.vocab[word].index = i
def reset_from(self, other_model):
"""
Borrow shareable pre-built structures (like vocab) from the other_model. Useful
if testing multiple models in parallel on the same corpus.
"""
self.wv.vocab = other_model.wv.vocab
self.wv.index2word = other_model.wv.index2word
self.cum_table = other_model.cum_table
self.corpus_count = other_model.corpus_count
self.reset_weights()
def _do_train_job(self, sentences, alpha, inits):
"""
Train a single batch of sentences. Return 2-tuple `(effective word count after
ignoring unknown words and sentence length trimming, total word count)`.
"""
work, neu1 = inits
tally = 0
if self.sg:
tally += train_batch_sg(self, sentences, alpha, work, self.compute_loss)
else:
tally += train_batch_cbow(self, sentences, alpha, work, neu1, self.compute_loss)
return tally, self._raw_word_count(sentences)
def _raw_word_count(self, job):
"""Return the number of words in a given job."""
return sum(len(sentence) for sentence in job)
def train(self, sentences, total_examples=None, total_words=None,
epochs=None, start_alpha=None, end_alpha=None, word_count=0,
queue_factor=2, report_delay=1.0, compute_loss=None):
"""
Update the model's neural weights from a sequence of sentences (can be a once-only generator stream).
For Word2Vec, each sentence must be a list of unicode strings. (Subclasses may accept other examples.)
To support linear learning-rate decay from (initial) alpha to min_alpha, and accurate
progres-percentage logging, either total_examples (count of sentences) or total_words (count of
raw words in sentences) MUST be provided. (If the corpus is the same as was provided to
`build_vocab()`, the count of examples in that corpus will be available in the model's
`corpus_count` property.)
To avoid common mistakes around the model's ability to do multiple training passes itself, an
explicit `epochs` argument MUST be provided. In the common and recommended case, where `train()`
is only called once, the model's cached `iter` value should be supplied as `epochs` value.
"""
if self.model_trimmed_post_training:
raise RuntimeError("Parameters for training were discarded using model_trimmed_post_training method")
if FAST_VERSION < 0:
warnings.warn(
"C extension not loaded for Word2Vec, training will be slow. "
"Install a C compiler and reinstall gensim for fast training."
)
self.neg_labels = []
if self.negative > 0:
# precompute negative labels optimization for pure-python training
self.neg_labels = zeros(self.negative + 1)
self.neg_labels[0] = 1.
if compute_loss:
self.compute_loss = compute_loss
self.running_training_loss = 0
logger.info(
"training model with %i workers on %i vocabulary and %i features, "
"using sg=%s hs=%s sample=%s negative=%s window=%s",
self.workers, len(self.wv.vocab), self.layer1_size, self.sg,
self.hs, self.sample, self.negative, self.window
)
if not self.wv.vocab:
raise RuntimeError("you must first build vocabulary before training the model")
if not len(self.wv.syn0):
raise RuntimeError("you must first finalize vocabulary before training the model")
if not hasattr(self, 'corpus_count'):
raise ValueError(
"The number of sentences in the training corpus is missing. "
"Did you load the model via KeyedVectors.load_word2vec_format?"
"Models loaded via load_word2vec_format don't support further training. "
"Instead start with a blank model, scan_vocab on the new corpus, "
"intersect_word2vec_format with the old model, then train."
)
if total_words is None and total_examples is None:
raise ValueError(
"You must specify either total_examples or total_words, for proper alpha and progress calculations. "
"The usual value is total_examples=model.corpus_count."
)
if epochs is None:
raise ValueError("You must specify an explict epochs count. The usual value is epochs=model.iter.")
start_alpha = start_alpha or self.alpha
end_alpha = end_alpha or self.min_alpha
job_tally = 0
if epochs > 1:
sentences = utils.RepeatCorpusNTimes(sentences, epochs)
total_words = total_words and total_words * epochs
total_examples = total_examples and total_examples * epochs
def worker_loop():
"""Train the model, lifting lists of sentences from the job_queue."""
work = matutils.zeros_aligned(self.layer1_size, dtype=REAL) # per-thread private work memory
neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL)
jobs_processed = 0
while True:
job = job_queue.get()
if job is None:
progress_queue.put(None)
break # no more jobs => quit this worker
sentences, alpha = job
tally, raw_tally = self._do_train_job(sentences, alpha, (work, neu1))
progress_queue.put((len(sentences), tally, raw_tally)) # report back progress
jobs_processed += 1
logger.debug("worker exiting, processed %i jobs", jobs_processed)
def job_producer():
"""Fill jobs queue using the input `sentences` iterator."""
job_batch, batch_size = [], 0
pushed_words, pushed_examples = 0, 0
next_alpha = start_alpha
if next_alpha > self.min_alpha_yet_reached:
logger.warning("Effective 'alpha' higher than previous training cycles")
self.min_alpha_yet_reached = next_alpha
job_no = 0
for sent_idx, sentence in enumerate(sentences):
sentence_length = self._raw_word_count([sentence])
# can we fit this sentence into the existing job batch?
if batch_size + sentence_length <= self.batch_words:
# yes => add it to the current job
job_batch.append(sentence)
batch_size += sentence_length
else:
# no => submit the existing job
logger.debug(
"queueing job #%i (%i words, %i sentences) at alpha %.05f",
job_no, batch_size, len(job_batch), next_alpha
)
job_no += 1
job_queue.put((job_batch, next_alpha))
# update the learning rate for the next job
if end_alpha < next_alpha:
if total_examples:
# examples-based decay
pushed_examples += len(job_batch)
progress = 1.0 * pushed_examples / total_examples
else:
# words-based decay
pushed_words += self._raw_word_count(job_batch)
progress = 1.0 * pushed_words / total_words
next_alpha = start_alpha - (start_alpha - end_alpha) * progress
next_alpha = max(end_alpha, next_alpha)
# add the sentence that didn't fit as the first item of a new job
job_batch, batch_size = [sentence], sentence_length
# add the last job too (may be significantly smaller than batch_words)
if job_batch:
logger.debug(
"queueing job #%i (%i words, %i sentences) at alpha %.05f",
job_no, batch_size, len(job_batch), next_alpha
)
job_no += 1
job_queue.put((job_batch, next_alpha))
if job_no == 0 and self.train_count == 0:
logger.warning(
"train() called with an empty iterator (if not intended, "
"be sure to provide a corpus that offers restartable iteration = an iterable)."
)
# give the workers heads up that they can finish -- no more work!
for _ in xrange(self.workers):
job_queue.put(None)
logger.debug("job loop exiting, total %i jobs", job_no)
# buffer ahead only a limited number of jobs.. this is the reason we can't simply use ThreadPool :(
job_queue = Queue(maxsize=queue_factor * self.workers)
progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers)
workers = [threading.Thread(target=worker_loop) for _ in xrange(self.workers)]
unfinished_worker_count = len(workers)
workers.append(threading.Thread(target=job_producer))
for thread in workers:
thread.daemon = True # make interrupting the process with ctrl+c easier
thread.start()
example_count, trained_word_count, raw_word_count = 0, 0, word_count
start, next_report = default_timer() - 0.00001, 1.0
while unfinished_worker_count > 0:
report = progress_queue.get() # blocks if workers too slow
if report is None: # a thread reporting that it finished
unfinished_worker_count -= 1
logger.info("worker thread finished; awaiting finish of %i more threads", unfinished_worker_count)
continue
examples, trained_words, raw_words = report
job_tally += 1
# update progress stats
example_count += examples
trained_word_count += trained_words # only words in vocab & sampled
raw_word_count += raw_words
# log progress once every report_delay seconds
elapsed = default_timer() - start
if elapsed >= next_report:
if total_examples:
# examples-based progress %
logger.info(
"PROGRESS: at %.2f%% examples, %.0f words/s, in_qsize %i, out_qsize %i",
100.0 * example_count / total_examples, trained_word_count / elapsed,
utils.qsize(job_queue), utils.qsize(progress_queue)
)
else:
# words-based progress %
logger.info(
"PROGRESS: at %.2f%% words, %.0f words/s, in_qsize %i, out_qsize %i",
100.0 * raw_word_count / total_words, trained_word_count / elapsed,
utils.qsize(job_queue), utils.qsize(progress_queue)
)
next_report = elapsed + report_delay
# all done; report the final stats
elapsed = default_timer() - start
logger.info(
"training on %i raw words (%i effective words) took %.1fs, %.0f effective words/s",
raw_word_count, trained_word_count, elapsed, trained_word_count / elapsed
)
if job_tally < 10 * self.workers:
logger.warning(
"under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay"
)
# check that the input corpus hasn't changed during iteration
if total_examples and total_examples != example_count:
logger.warning(
"supplied example count (%i) did not equal expected count (%i)", example_count, total_examples
)
if total_words and total_words != raw_word_count:
logger.warning(
"supplied raw word count (%i) did not equal expected count (%i)", raw_word_count, total_words
)
self.train_count += 1 # number of times train() has been called
self.total_train_time += elapsed
self.clear_sims()
return trained_word_count
# basics copied from the train() function
def score(self, sentences, total_sentences=int(1e6), chunksize=100, queue_factor=2, report_delay=1):
"""
Score the log probability for a sequence of sentences (can be a once-only generator stream).
Each sentence must be a list of unicode strings.
This does not change the fitted model in any way (see Word2Vec.train() for that).
We have currently only implemented score for the hierarchical softmax scheme,
so you need to have run word2vec with hs=1 and negative=0 for this to work.
Note that you should specify total_sentences; we'll run into problems if you ask to
score more than this number of sentences but it is inefficient to set the value too high.
See the article by [#taddy]_ and the gensim demo at [#deepir]_ for examples of
how to use such scores in document classification.
.. [#taddy] Taddy, Matt. Document Classification by Inversion of Distributed Language Representations,
in Proceedings of the 2015 Conference of the Association of Computational Linguistics.
.. [#deepir] https://github.com/piskvorky/gensim/blob/develop/docs/notebooks/deepir.ipynb
"""
if FAST_VERSION < 0:
warnings.warn(
"C extension compilation failed, scoring will be slow. "
"Install a C compiler and reinstall gensim for fastness."
)
logger.info(
"scoring sentences with %i workers on %i vocabulary and %i features, "
"using sg=%s hs=%s sample=%s and negative=%s",
self.workers, len(self.wv.vocab), self.layer1_size, self.sg, self.hs, self.sample, self.negative
)
if not self.wv.vocab:
raise RuntimeError("you must first build vocabulary before scoring new data")
if not self.hs:
raise RuntimeError(
"We have currently only implemented score for the hierarchical softmax scheme, "
"so you need to have run word2vec with hs=1 and negative=0 for this to work."
)
def worker_loop():
"""Compute log probability for each sentence, lifting lists of sentences from the jobs queue."""
work = zeros(1, dtype=REAL) # for sg hs, we actually only need one memory loc (running sum)
neu1 = matutils.zeros_aligned(self.layer1_size, dtype=REAL)
while True:
job = job_queue.get()
if job is None: # signal to finish
break
ns = 0
for sentence_id, sentence in job:
if sentence_id >= total_sentences:
break
if self.sg:
score = score_sentence_sg(self, sentence, work)
else:
score = score_sentence_cbow(self, sentence, work, neu1)
sentence_scores[sentence_id] = score
ns += 1
progress_queue.put(ns) # report progress
start, next_report = default_timer(), 1.0
# buffer ahead only a limited number of jobs.. this is the reason we can't simply use ThreadPool :(
job_queue = Queue(maxsize=queue_factor * self.workers)
progress_queue = Queue(maxsize=(queue_factor + 1) * self.workers)
workers = [threading.Thread(target=worker_loop) for _ in xrange(self.workers)]
for thread in workers:
thread.daemon = True # make interrupting the process with ctrl+c easier
thread.start()
sentence_count = 0
sentence_scores = matutils.zeros_aligned(total_sentences, dtype=REAL)
push_done = False
done_jobs = 0
jobs_source = enumerate(utils.grouper(enumerate(sentences), chunksize))
# fill jobs queue with (id, sentence) job items
while True:
try:
job_no, items = next(jobs_source)
if (job_no - 1) * chunksize > total_sentences:
logger.warning(
"terminating after %i sentences (set higher total_sentences if you want more).",
total_sentences
)
job_no -= 1
raise StopIteration()
logger.debug("putting job #%i in the queue", job_no)
job_queue.put(items)
except StopIteration:
logger.info("reached end of input; waiting to finish %i outstanding jobs", job_no - done_jobs + 1)
for _ in xrange(self.workers):
job_queue.put(None) # give the workers heads up that they can finish -- no more work!
push_done = True
try:
while done_jobs < (job_no + 1) or not push_done:
ns = progress_queue.get(push_done) # only block after all jobs pushed
sentence_count += ns
done_jobs += 1
elapsed = default_timer() - start
if elapsed >= next_report:
logger.info(
"PROGRESS: at %.2f%% sentences, %.0f sentences/s",
100.0 * sentence_count, sentence_count / elapsed
)
next_report = elapsed + report_delay # don't flood log, wait report_delay seconds
else:
# loop ended by job count; really done
break
except Empty:
pass # already out of loop; continue to next push
elapsed = default_timer() - start
self.clear_sims()
logger.info(
"scoring %i sentences took %.1fs, %.0f sentences/s",
sentence_count, elapsed, sentence_count / elapsed
)
return sentence_scores[:sentence_count]
def clear_sims(self):
"""
Removes all L2-normalized vectors for words from the model.
You will have to recompute them using init_sims method.
"""
self.wv.syn0norm = None
def update_weights(self):
"""
Copy all the existing weights, and reset the weights for the newly
added vocabulary.
"""
logger.info("updating layer weights")
gained_vocab = len(self.wv.vocab) - len(self.wv.syn0)
newsyn0 = empty((gained_vocab, self.vector_size), dtype=REAL)
# randomize the remaining words
for i in xrange(len(self.wv.syn0), len(self.wv.vocab)):
# construct deterministic seed from word AND seed argument
newsyn0[i - len(self.wv.syn0)] = self.seeded_vector(self.wv.index2word[i] + str(self.seed))
# Raise an error if an online update is run before initial training on a corpus
if not len(self.wv.syn0):
raise RuntimeError(
"You cannot do an online vocabulary-update of a model which has no prior vocabulary. "
"First build the vocabulary of your model with a corpus before doing an online update."
)
self.wv.syn0 = vstack([self.wv.syn0, newsyn0])
if self.hs:
self.syn1 = vstack([self.syn1, zeros((gained_vocab, self.layer1_size), dtype=REAL)])
if self.negative:
self.syn1neg = vstack([self.syn1neg, zeros((gained_vocab, self.layer1_size), dtype=REAL)])
self.wv.syn0norm = None
# do not suppress learning for already learned words
self.syn0_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning
def reset_weights(self):
"""Reset all projection weights to an initial (untrained) state, but keep the existing vocabulary."""
logger.info("resetting layer weights")
self.wv.syn0 = empty((len(self.wv.vocab), self.vector_size), dtype=REAL)
# randomize weights vector by vector, rather than materializing a huge random matrix in RAM at once
for i in xrange(len(self.wv.vocab)):
# construct deterministic seed from word AND seed argument
self.wv.syn0[i] = self.seeded_vector(self.wv.index2word[i] + str(self.seed))
if self.hs:
self.syn1 = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL)
if self.negative:
self.syn1neg = zeros((len(self.wv.vocab), self.layer1_size), dtype=REAL)
self.wv.syn0norm = None
self.syn0_lockf = ones(len(self.wv.vocab), dtype=REAL) # zeros suppress learning
def seeded_vector(self, seed_string):
"""Create one 'random' vector (but deterministic by seed_string)"""
# Note: built-in hash() may vary by Python version or even (in Py3.x) per launch
once = random.RandomState(self.hashfxn(seed_string) & 0xffffffff)
return (once.rand(self.vector_size) - 0.5) / self.vector_size
def intersect_word2vec_format(self, fname, lockf=0.0, binary=False, encoding='utf8', unicode_errors='strict'):
"""
Merge the input-hidden weight matrix from the original C word2vec-tool format
given, where it intersects with the current vocabulary. (No words are added to the
existing vocabulary, but intersecting words adopt the file's weights, and
non-intersecting words are left alone.)
`binary` is a boolean indicating whether the data is in binary word2vec format.
`lockf` is a lock-factor value to be set for any imported word-vectors; the
default value of 0.0 prevents further updating of the vector during subsequent
training. Use 1.0 to allow further training updates of merged vectors.
"""
overlap_count = 0
logger.info("loading projection weights from %s", fname)
with utils.smart_open(fname) as fin:
header = utils.to_unicode(fin.readline(), encoding=encoding)
vocab_size, vector_size = (int(x) for x in header.split()) # throws for invalid file format
if not vector_size == self.vector_size:
raise ValueError("incompatible vector size %d in file %s" % (vector_size, fname))
# TOCONSIDER: maybe mismatched vectors still useful enough to merge (truncating/padding)?
if binary:
binary_len = dtype(REAL).itemsize * vector_size
for _ in xrange(vocab_size):
# mixed text and binary: read text first, then binary
word = []
while True:
ch = fin.read(1)
if ch == b' ':
break
if ch != b'\n': # ignore newlines in front of words (some binary files have)
word.append(ch)
word = utils.to_unicode(b''.join(word), encoding=encoding, errors=unicode_errors)
weights = fromstring(fin.read(binary_len), dtype=REAL)
if word in self.wv.vocab:
overlap_count += 1
self.wv.syn0[self.wv.vocab[word].index] = weights
self.syn0_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0 stops further changes
else:
for line_no, line in enumerate(fin):
parts = utils.to_unicode(line.rstrip(), encoding=encoding, errors=unicode_errors).split(" ")
if len(parts) != vector_size + 1:
raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no)
word, weights = parts[0], [REAL(x) for x in parts[1:]]
if word in self.wv.vocab:
overlap_count += 1
self.wv.syn0[self.wv.vocab[word].index] = weights
self.syn0_lockf[self.wv.vocab[word].index] = lockf # lock-factor: 0.0 stops further changes
logger.info("merged %d vectors into %s matrix from %s", overlap_count, self.wv.syn0.shape, fname)
def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=None, indexer=None):
"""
Deprecated. Use self.wv.most_similar() instead.
Refer to the documentation for `gensim.models.KeyedVectors.most_similar`
"""
return self.wv.most_similar(positive, negative, topn, restrict_vocab, indexer)
def wmdistance(self, document1, document2):
"""
Deprecated. Use self.wv.wmdistance() instead.
Refer to the documentation for `gensim.models.KeyedVectors.wmdistance`
"""
return self.wv.wmdistance(document1, document2)
def most_similar_cosmul(self, positive=None, negative=None, topn=10):
"""
Deprecated. Use self.wv.most_similar_cosmul() instead.
Refer to the documentation for `gensim.models.KeyedVectors.most_similar_cosmul`
"""
return self.wv.most_similar_cosmul(positive, negative, topn)
def similar_by_word(self, word, topn=10, restrict_vocab=None):
"""
Deprecated. Use self.wv.similar_by_word() instead.
Refer to the documentation for `gensim.models.KeyedVectors.similar_by_word`
"""
return self.wv.similar_by_word(word, topn, restrict_vocab)
def similar_by_vector(self, vector, topn=10, restrict_vocab=None):
"""
Deprecated. Use self.wv.similar_by_vector() instead.
Refer to the documentation for `gensim.models.KeyedVectors.similar_by_vector`
"""
return self.wv.similar_by_vector(vector, topn, restrict_vocab)
def doesnt_match(self, words):
"""
Deprecated. Use self.wv.doesnt_match() instead.
Refer to the documentation for `gensim.models.KeyedVectors.doesnt_match`
"""
return self.wv.doesnt_match(words)
def __getitem__(self, words):
"""
Deprecated. Use self.wv.__getitem__() instead.
Refer to the documentation for `gensim.models.KeyedVectors.__getitem__`
"""
return self.wv.__getitem__(words)
def __contains__(self, word):
"""
Deprecated. Use self.wv.__contains__() instead.
Refer to the documentation for `gensim.models.KeyedVectors.__contains__`
"""
return self.wv.__contains__(word)
def similarity(self, w1, w2):
"""
Deprecated. Use self.wv.similarity() instead.
Refer to the documentation for `gensim.models.KeyedVectors.similarity`
"""
return self.wv.similarity(w1, w2)
def n_similarity(self, ws1, ws2):
"""
Deprecated. Use self.wv.n_similarity() instead.
Refer to the documentation for `gensim.models.KeyedVectors.n_similarity`
"""
return self.wv.n_similarity(ws1, ws2)
def predict_output_word(self, context_words_list, topn=10):
"""Report the probability distribution of the center word given the context words
as input to the trained model."""
if not self.negative:
raise RuntimeError(
"We have currently only implemented predict_output_word for the negative sampling scheme, "
"so you need to have run word2vec with negative > 0 for this to work."
)
if not hasattr(self.wv, 'syn0') or not hasattr(self, 'syn1neg'):
raise RuntimeError("Parameters required for predicting the output words not found.")
word_vocabs = [self.wv.vocab[w] for w in context_words_list if w in self.wv.vocab]
if not word_vocabs:
warnings.warn("All the input context words are out-of-vocabulary for the current model.")
return None
word2_indices = [word.index for word in word_vocabs]
l1 = np_sum(self.wv.syn0[word2_indices], axis=0)
if word2_indices and self.cbow_mean:
l1 /= len(word2_indices)
prob_values = exp(dot(l1, self.syn1neg.T)) # propagate hidden -> output and take softmax to get probabilities
prob_values /= sum(prob_values)
top_indices = matutils.argsort(prob_values, topn=topn, reverse=True)
# returning the most probable output words with their probabilities
return [(self.wv.index2word[index1], prob_values[index1]) for index1 in top_indices]
def init_sims(self, replace=False):
"""
init_sims() resides in KeyedVectors because it deals with syn0 mainly, but because syn1 is not an attribute
of KeyedVectors, it has to be deleted in this class, and the normalizing of syn0 happens inside of KeyedVectors
"""
if replace and hasattr(self, 'syn1'):
del self.syn1
return self.wv.init_sims(replace)
def estimate_memory(self, vocab_size=None, report=None):
"""Estimate required memory for a model using current settings and provided vocabulary size."""
vocab_size = vocab_size or len(self.wv.vocab)
report = report or {}
report['vocab'] = vocab_size * (700 if self.hs else 500)
report['syn0'] = vocab_size * self.vector_size * dtype(REAL).itemsize
if self.hs:
report['syn1'] = vocab_size * self.layer1_size * dtype(REAL).itemsize
if self.negative:
report['syn1neg'] = vocab_size * self.layer1_size * dtype(REAL).itemsize
report['total'] = sum(report.values())
logger.info(
"estimated required memory for %i words and %i dimensions: %i bytes",
vocab_size, self.vector_size, report['total']
)
return report
@staticmethod
def log_accuracy(section):
return KeyedVectors.log_accuracy(section)
def accuracy(self, questions, restrict_vocab=30000, most_similar=None, case_insensitive=True):
most_similar = most_similar or KeyedVectors.most_similar
return self.wv.accuracy(questions, restrict_vocab, most_similar, case_insensitive)
@staticmethod
def log_evaluate_word_pairs(pearson, spearman, oov, pairs):
"""
Deprecated. Use self.wv.log_evaluate_word_pairs() instead.
Refer to the documentation for `gensim.models.KeyedVectors.log_evaluate_word_pairs`
"""
return KeyedVectors.log_evaluate_word_pairs(pearson, spearman, oov, pairs)
def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000,
case_insensitive=True, dummy4unknown=False):
"""
Deprecated. Use self.wv.evaluate_word_pairs() instead.
Refer to the documentation for `gensim.models.KeyedVectors.evaluate_word_pairs`
"""
return self.wv.evaluate_word_pairs(pairs, delimiter, restrict_vocab, case_insensitive, dummy4unknown)
def __str__(self):
return "%s(vocab=%s, size=%s, alpha=%s)" % (
self.__class__.__name__, len(self.wv.index2word), self.vector_size, self.alpha
)
def _minimize_model(self, save_syn1=False, save_syn1neg=False, save_syn0_lockf=False):
warnings.warn(
"This method would be deprecated in the future. "
"Keep just_word_vectors = model.wv to retain just the KeyedVectors instance "
"for read-only querying of word vectors."
)
if save_syn1 and save_syn1neg and save_syn0_lockf:
return
if hasattr(self, 'syn1') and not save_syn1:
del self.syn1
if hasattr(self, 'syn1neg') and not save_syn1neg:
del self.syn1neg
if hasattr(self, 'syn0_lockf') and not save_syn0_lockf:
del self.syn0_lockf
self.model_trimmed_post_training = True
def delete_temporary_training_data(self, replace_word_vectors_with_normalized=False):
"""
Discard parameters that are used in training and score. Use if you're sure you're done training a model.
If `replace_word_vectors_with_normalized` is set, forget the original vectors and only keep the normalized
ones = saves lots of memory!
"""
if replace_word_vectors_with_normalized:
self.init_sims(replace=True)
self._minimize_model()
def save(self, *args, **kwargs):
# don't bother storing the cached normalized vectors, recalculable table
kwargs['ignore'] = kwargs.get('ignore', ['syn0norm', 'table', 'cum_table'])
super(Word2Vec, self).save(*args, **kwargs)
save.__doc__ = SaveLoad.save.__doc__
@classmethod
def load(cls, *args, **kwargs):
model = super(Word2Vec, cls).load(*args, **kwargs)
# update older models
if hasattr(model, 'table'):
delattr(model, 'table') # discard in favor of cum_table
if model.negative and hasattr(model.wv, 'index2word'):
model.make_cum_table() # rebuild cum_table from vocabulary
if not hasattr(model, 'corpus_count'):
model.corpus_count = None
for v in model.wv.vocab.values():
if hasattr(v, 'sample_int'):
break # already 0.12.0+ style int probabilities
elif hasattr(v, 'sample_probability'):
v.sample_int = int(round(v.sample_probability * 2**32))
del v.sample_probability
if not hasattr(model, 'syn0_lockf') and hasattr(model, 'syn0'):
model.syn0_lockf = ones(len(model.wv.syn0), dtype=REAL)
if not hasattr(model, 'random'):
model.random = random.RandomState(model.seed)
if not hasattr(model, 'train_count'):
model.train_count = 0
model.total_train_time = 0
return model
def _load_specials(self, *args, **kwargs):
super(Word2Vec, self)._load_specials(*args, **kwargs)
# loading from a pre-KeyedVectors word2vec model
if not hasattr(self, 'wv'):
wv = KeyedVectors()
wv.syn0 = self.__dict__.get('syn0', [])
wv.syn0norm = self.__dict__.get('syn0norm', None)
wv.vocab = self.__dict__.get('vocab', {})
wv.index2word = self.__dict__.get('index2word', [])
self.wv = wv
@classmethod
def load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',
limit=None, datatype=REAL):
"""Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead."""
raise DeprecationWarning("Deprecated. Use gensim.models.KeyedVectors.load_word2vec_format instead.")
def save_word2vec_format(self, fname, fvocab=None, binary=False):
"""Deprecated. Use model.wv.save_word2vec_format instead."""
raise DeprecationWarning("Deprecated. Use model.wv.save_word2vec_format instead.")
def get_latest_training_loss(self):
return self.running_training_loss
class BrownCorpus(object):
"""Iterate over sentences from the Brown corpus (part of NLTK data)."""
def __init__(self, dirname):
self.dirname = dirname
def __iter__(self):
for fname in os.listdir(self.dirname):
fname = os.path.join(self.dirname, fname)
if not os.path.isfile(fname):
continue
for line in utils.smart_open(fname):
line = utils.to_unicode(line)
# each file line is a single sentence in the Brown corpus
# each token is WORD/POS_TAG
token_tags = [t.split('/') for t in line.split() if len(t.split('/')) == 2]
# ignore words with non-alphabetic tags like ",", "!" etc (punctuation, weird stuff)
words = ["%s/%s" % (token.lower(), tag[:2]) for token, tag in token_tags if tag[:2].isalpha()]
if not words: # don't bother sending out empty sentences
continue
yield words
class Text8Corpus(object):
"""Iterate over sentences from the "text8" corpus, unzipped from http://mattmahoney.net/dc/text8.zip ."""
def __init__(self, fname, max_sentence_length=MAX_WORDS_IN_BATCH):
self.fname = fname
self.max_sentence_length = max_sentence_length
def __iter__(self):
# the entire corpus is one gigantic line -- there are no sentence marks at all
# so just split the sequence of tokens arbitrarily: 1 sentence = 1000 tokens
sentence, rest = [], b''
with utils.smart_open(self.fname) as fin:
while True:
text = rest + fin.read(8192) # avoid loading the entire file (=1 line) into RAM
if text == rest: # EOF
words = utils.to_unicode(text).split()
sentence.extend(words) # return the last chunk of words, too (may be shorter/longer)
if sentence:
yield sentence
break
last_token = text.rfind(b' ') # last token may have been split in two... keep for next iteration
words, rest = (utils.to_unicode(text[:last_token]).split(),
text[last_token:].strip()) if last_token >= 0 else ([], text)
sentence.extend(words)
while len(sentence) >= self.max_sentence_length:
yield sentence[:self.max_sentence_length]
sentence = sentence[self.max_sentence_length:]
class LineSentence(object):
"""
Simple format: one sentence = one line; words already preprocessed and separated by whitespace.
"""
def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None):
"""
`source` can be either a string or a file object. Clip the file to the first
`limit` lines (or not clipped if limit is None, the default).
Example::
sentences = LineSentence('myfile.txt')
Or for compressed files::
sentences = LineSentence('compressed_text.txt.bz2')
sentences = LineSentence('compressed_text.txt.gz')
"""
self.source = source
self.max_sentence_length = max_sentence_length
self.limit = limit
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 itertools.islice(self.source, self.limit):
line = utils.to_unicode(line).split()
i = 0
while i < len(line):
yield line[i: i + self.max_sentence_length]
i += self.max_sentence_length
except AttributeError:
# If it didn't work like a file, use it as a string filename
with utils.smart_open(self.source) as fin:
for line in itertools.islice(fin, self.limit):
line = utils.to_unicode(line).split()
i = 0
while i < len(line):
yield line[i: i + self.max_sentence_length]
i += self.max_sentence_length
class PathLineSentences(object):
"""
Works like word2vec.LineSentence, but will process all files in a directory in alphabetical order by filename.
The directory can only contain files that can be read by LineSentence: .bz2, .gz, and text files.
Any file not ending with .bz2 or .gz is assumed to be a text file. Does not work with subdirectories.
The format of files (either text, or compressed text files) in the path is one sentence = one line,
with words already preprocessed and separated by whitespace.
"""
def __init__(self, source, max_sentence_length=MAX_WORDS_IN_BATCH, limit=None):
"""
`source` should be a path to a directory (as a string) where all files can be opened by the
LineSentence class. Each file will be read up to `limit` lines (or not clipped if limit is None, the default).
Example::
sentences = PathLineSentences(os.getcwd() + '\\corpus\\')
The files in the directory should be either text files, .bz2 files, or .gz files.
"""
self.source = source
self.max_sentence_length = max_sentence_length
self.limit = limit
if os.path.isfile(self.source):
logger.debug('single file given as source, rather than a directory of files')
logger.debug('consider using models.word2vec.LineSentence for a single file')
self.input_files = [self.source] # force code compatibility with list of files
elif os.path.isdir(self.source):
self.source = os.path.join(self.source, '') # ensures os-specific slash at end of path
logger.info('reading directory %s', self.source)
self.input_files = os.listdir(self.source)
self.input_files = [self.source + filename for filename in self.input_files] # make full paths
self.input_files.sort() # makes sure it happens in filename order
else: # not a file or a directory, then we can't do anything with it
raise ValueError('input is neither a file nor a path')
logger.info('files read into PathLineSentences:%s', '\n'.join(self.input_files))
def __iter__(self):
"""iterate through the files"""
for file_name in self.input_files:
logger.info('reading file %s', file_name)
with utils.smart_open(file_name) as fin:
for line in itertools.islice(fin, self.limit):
line = utils.to_unicode(line).split()
i = 0
while i < len(line):
yield line[i:i + self.max_sentence_length]
i += self.max_sentence_length
# Example: ./word2vec.py -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 \
# -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3
if __name__ == "__main__":
import argparse
logging.basicConfig(
format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s',
level=logging.INFO
)
logger.info("running %s", " ".join(sys.argv))
logger.info("using optimization %s", FAST_VERSION)
# check and process cmdline input
program = os.path.basename(sys.argv[0])
if len(sys.argv) < 2:
print(globals()['__doc__'] % locals())
sys.exit(1)
from gensim.models.word2vec import Word2Vec # noqa:F811 avoid referencing __main__ in pickle
seterr(all='raise') # don't ignore numpy errors
parser = argparse.ArgumentParser()
parser.add_argument("-train", help="Use text data from file TRAIN to train the model", required=True)
parser.add_argument("-output", help="Use file OUTPUT to save the resulting word vectors")
parser.add_argument("-window", help="Set max skip length WINDOW between words; default is 5", type=int, default=5)
parser.add_argument("-size", help="Set size of word vectors; default is 100", type=int, default=100)
parser.add_argument(
"-sample",
help="Set threshold for occurrence of words. "
"Those that appear with higher frequency in the training data will be randomly down-sampled;"
" default is 1e-3, useful range is (0, 1e-5)",
type=float, default=1e-3
)
parser.add_argument(
"-hs", help="Use Hierarchical Softmax; default is 0 (not used)",
type=int, default=0, choices=[0, 1]
)
parser.add_argument(
"-negative", help="Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)",
type=int, default=5
)
parser.add_argument("-threads", help="Use THREADS threads (default 12)", type=int, default=12)
parser.add_argument("-iter", help="Run more training iterations (default 5)", type=int, default=5)
parser.add_argument(
"-min_count", help="This will discard words that appear less than MIN_COUNT times; default is 5",
type=int, default=5
)
parser.add_argument(
"-cbow", help="Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)",
type=int, default=1, choices=[0, 1]
)
parser.add_argument(
"-binary", help="Save the resulting vectors in binary mode; default is 0 (off)",
type=int, default=0, choices=[0, 1]
)
parser.add_argument("-accuracy", help="Use questions from file ACCURACY to evaluate the model")
args = parser.parse_args()
if args.cbow == 0:
skipgram = 1
else:
skipgram = 0
corpus = LineSentence(args.train)
model = Word2Vec(
corpus, size=args.size, min_count=args.min_count, workers=args.threads,
window=args.window, sample=args.sample, sg=skipgram, hs=args.hs,
negative=args.negative, cbow_mean=1, iter=args.iter
)
if args.output:
outfile = args.output
model.wv.save_word2vec_format(outfile, binary=args.binary)
else:
outfile = args.train
model.save(outfile + '.model')
if args.binary == 1:
model.wv.save_word2vec_format(outfile + '.model.bin', binary=True)
else:
model.wv.save_word2vec_format(outfile + '.model.txt', binary=False)
if args.accuracy:
model.accuracy(args.accuracy)
logger.info("finished running %s", program)
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