Word2Vec 源码解析+范例

#!/usr/bin/env python# -*- coding: utf-8 -*-## Copyright (C) 2013 Radim Rehurek <[e-mail]># Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html"""Produce word vectors with deep learning via word2vec's "skip-gram and CBOW models", using eitherhierarchical 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 formatYou can perform various NLP word tasks with the model. Some of themare 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')  0.73723527Probability of a text under the model::  >>> model.score(["The fox jumped over a lazy dog".split()])  0.2158356Correlation with human opinion on word similarity::  >>> model.wv.evaluate_word_pairs(os.path.join(module_path, 'test_data','wordsim353.tsv'))  0.51, 0.62, 0.13And 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 modelto trim unneeded model memory = use much less RAM.Note that there is a :mod:`gensim.models.phrases` module which lets you automaticallydetect phrases longer than one word. Using phrases, you can learn a word2vec modelwhere "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 loggingimport sysimport osimport heapqfrom timeit import default_timerfrom copy import deepcopyfrom collections import defaultdictimport threadingimport itertoolsimport warningsfrom gensim.utils import keep_vocab_item, call_on_class_onlyfrom gensim.models.keyedvectors import KeyedVectors, Vocabtry:    from queue import Queue, Emptyexcept ImportError:    from Queue import Queue, Emptyfrom numpy import exp, log, dot, zeros, outer, random, dtype, float32 as REAL,\    double, uint32, seterr, array, uint8, vstack, fromstring, sqrt, newaxis,\    ndarray, empty, sum as np_sum, prod, ones, ascontiguousarray, vstack, logaddexpfrom scipy.special import expitfrom gensim import utils, matutils  # utility fnc for pickling, common scipy operations etcfrom gensim.corpora.dictionary import Dictionaryfrom six import iteritems, itervalues, string_typesfrom six.moves import xrangefrom types import GeneratorTypefrom scipy import statslogger = logging.getLogger(__name__)try:    from gensim.models.word2vec_inner import train_batch_sg, train_batch_cbow    from gensim.models.word2vec_inner import score_sentence_sg, score_sentence_cbow    from gensim.models.word2vec_inner import FAST_VERSION, MAX_WORDS_IN_BATCHexcept ImportError:    # failed... fall back to plain numpy (20-80x slower training than the above)    FAST_VERSION = -1    MAX_WORDS_IN_BATCH = 10000    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_sentencedef train_sg_pair(model, word, context_index, alpha, learn_vectors=True, learn_hidden=True,                  context_vectors=None, context_locks=None, compute_loss=False):    if context_vectors is None:        context_vectors = model.wv.syn0    if context_locks is None:        context_locks = model.syn0_lockf    if word not in model.wv.vocab:        return    predict_word = model.wv.vocab[word]  # target word (NN output)    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:        l1 += neu1e * lock_factor  # learn input -> hidden (mutates model.wv.syn0[word2.index], if that is l1)    return neu1edef train_cbow_pair(model, word, input_word_indices, l1, alpha, learn_vectors=True, learn_hidden=True, compute_loss=False):    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 not model.cbow_mean and input_word_indices:            neu1e /= len(input_word_indices)        for i in input_word_indices:            model.wv.syn0[i] += neu1e * model.syn0_lockf[i]    return neu1edef 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(utils.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 {0} is being used'.format(__name__))        else:            logger.debug('Fast version of {0} is being used'.format(__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. ")                logger.warning("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        self.scale_vocab(keep_raw_vocab=keep_raw_vocab, trim_rule=trim_rule, update=update)  # trim by min_count & precalculate downsampling        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, sum(itervalues(vocab)) + total_words, len(vocab))            for word in sentence:                vocab[word] += 1            if self.max_vocab_size and len(vocab) > self.max_vocab_size:                total_words += utils.prune_vocab(vocab, min_reduce, trim_rule=trim_rule)                min_reduce += 1        total_words += sum(itervalues(vocab))        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    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        report_values = {'drop_unique': drop_unique, 'retain_total': retain_total,                         'downsample_unique': downsample_unique, 'downsample_total': int(downsample_total)}        # print extra memory estimates        report_values['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 = map(int, 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 line_no 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], list(map(REAL, 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=[], negative=[], 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=[], negative=[], 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)        return [(self.wv.index2word[index1], prob_values[index1]) for index1 in top_indices]   #returning the most probable output words with their probabilities    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__ = utils.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_lossclass 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 wordsclass 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 no 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_lengthclass PathLineSentences(object):    """    Simple format: one sentence = one line; words already preprocessed and separated by whitespace.    Like LineSentence, but will process all files in a directory in alphabetical order by filename    """    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 no clipped if limit is None, the default).        Example::            sentences = LineSentencePath(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):            logging.warning('single file read, better to use models.word2vec.LineSentence')            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            logging.debug('reading directory ' + self.source)            self.input_files = os.listdir(self.source)            self.input_files = [self.source + file for file 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')        logging.info('files read into PathLineSentences:' + '\n'.join(self.input_files))    def __iter__(self):        '''iterate through the files'''        for file_name in self.input_files:            logging.info('reading file ' + 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 3if __name__ == "__main__":    import argparse    logging.basicConfig(        format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s',        level=logging.INFO)    logging.info("running %s", " ".join(sys.argv))    logging.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  # 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)