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Word2Vecの進化形Doc2Vecで文章と文章の類似度を算出する

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米googleの研究者が開発した「 Word2Vec 」という技術をベースに、「単語」だけではなく「文書」にも意味を持たせてベクトルとして捉えて利用できる技術「 Doc2Vec 」をいじってみました。

Word2Vecのおさらい

過去Qiitaに投稿したので、そのリンクを張っておきます。
http://qiita.com/okappy/items/e16639178ba85edfee72

Doc2Vecとは?

Word2VecはWord(単語)をベクトルとして捉えるが、Doc2Vec(Paragraph2Vec)はDocument(文書)をWordの集合として見てベクトルを割り当てることで、文書間の類似度やベクトル計算などを実現することができる。

例えば、ニュース記事同士の類似度、レジュメ同士の類似度、本同士の類似度、もちろん人のプロフィールと本の類似度なども算出することができ、テキストで表されて者同士であれば、全てが対象となる。

技術的には

  • python
    • Scipy
    • gensim

あたりを使います。

gensimとは?

Pythonから扱える自然言語処理ライブラリで、
機能としては、以下のようなものが挙げられる。

  • 潜在意味解析(LSA/LSI/SVD)
  • 潜在ディリクレ配分法(LDA)
  • TF-IDF
  • Random Projection(RP)
  • 階層的ディリクレ過程(HDP)
  • 深層学習を用いたword2vec
  • 分散コンピューティング
  • Dynamic Topic Model(DTM)
  • Dynamic Influence Models(DIM)

gensimの公式ページ
http://radimrehurek.com/gensim/

実際に文書間の類似度を出してみる

今回は、facebookのデータを利用して、あるユーザーが過去facebookに投稿したテキストやシェアしたリンクのタイトルなどを一つの文書と見立てて、その文書同士(要するにユーザー同士)の類似度を出してみる。

実装(準備)

■ Scipyをインストール

pip install scipy

■ gensimのインストール

pip install gensim

■ doc2vec.pyをカスタマイズ

変更点①
デフォルトのdoc2vec.pyだと、レスポンスのときのlabelがカスタマイズできなかったので、
設定したlabelで結果を呼び出せるように変更してみました。

変更点②
doc2vec.pyのデフォルトでは、文書の似ているものは?って叩くと、文書も単語も出力されてしまうので、文書の似ている文書だけを出力するメソッドも作成しました。

doc2vec.py
#!/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


"""
Deep learning via the distributed memory and distributed bag of words models from
[1]_, using either hierarchical softmax or negative sampling [2]_ [3]_.

**Make sure you have a C compiler before installing gensim, to use optimized (compiled)
doc2vec training** (70x speedup [blog]_).

Initialize a model with e.g.::

>>> model = Doc2Vec(sentences, size=100, window=8, min_count=5, workers=4)

Persist a model to disk with::

>>> model.save(fname)
>>> model = Doc2Vec.load(fname)  # you can continue training with the loaded model!

The model can also be instantiated from an existing file on disk in the word2vec C format::

  >>> model = Doc2Vec.load_word2vec_format('/tmp/vectors.txt', binary=False)  # C text format
  >>> model = Doc2Vec.load_word2vec_format('/tmp/vectors.bin', binary=True)  # C binary format

.. [1] Quoc Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. http://arxiv.org/pdf/1405.4053v2.pdf
.. [2] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013.
.. [3] 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.
.. [blog] Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/

"""

import logging
import os

try:
    from queue import Queue
except ImportError:
    from Queue import Queue

from numpy import zeros, random, sum as np_sum

logger = logging.getLogger(__name__)

from gensim import utils  # utility fnc for pickling, common scipy operations etc
from gensim.models.word2vec import Word2Vec, Vocab, train_cbow_pair, train_sg_pair

try:
    from gensim.models.doc2vec_inner import train_sentence_dbow, train_sentence_dm, FAST_VERSION
except:
    # failed... fall back to plain numpy (20-80x slower training than the above)
    FAST_VERSION = -1

    def train_sentence_dbow(model, sentence, lbls, alpha, work=None, train_words=True, train_lbls=True):
        """
        Update distributed bag of words model by training on a single sentence.

        The sentence is a list of Vocab objects (or None, where the corresponding
        word is not in the vocabulary. Called internally from `Doc2Vec.train()`.

        This is the non-optimized, Python version. If you have cython installed, gensim
        will use the optimized version from doc2vec_inner instead.

        """
        neg_labels = []
        if model.negative:
            # precompute negative labels
            neg_labels = zeros(model.negative + 1)
            neg_labels[0] = 1.0

        for label in lbls:
            if label is None:
                continue  # OOV word in the input sentence => skip
            for word in sentence:
                if word is None:
                    continue  # OOV word in the input sentence => skip
                train_sg_pair(model, word, label, alpha, neg_labels, train_words, train_lbls)

        return len([word for word in sentence if word is not None])

    def train_sentence_dm(model, sentence, lbls, alpha, work=None, neu1=None, train_words=True, train_lbls=True):
        """
        Update distributed memory model by training on a single sentence.

        The sentence is a list of Vocab objects (or None, where the corresponding
        word is not in the vocabulary. Called internally from `Doc2Vec.train()`.

        This is the non-optimized, Python version. If you have a C compiler, gensim
        will use the optimized version from doc2vec_inner instead.

        """
        lbl_indices = [lbl.index for lbl in lbls if lbl is not None]
        lbl_sum = np_sum(model.syn0[lbl_indices], axis=0)
        lbl_len = len(lbl_indices)
        neg_labels = []
        if model.negative:
            # precompute negative labels
            neg_labels = zeros(model.negative + 1)
            neg_labels[0] = 1.

        for pos, word in enumerate(sentence):
            if word is None:
                continue  # OOV word in the input sentence => skip
            reduced_window = random.randint(model.window)  # `b` in the original doc2vec code
            start = max(0, pos - model.window + reduced_window)
            window_pos = enumerate(sentence[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.syn0[word2_indices], axis=0) + lbl_sum  # 1 x layer1_size
            if word2_indices and model.cbow_mean:
                l1 /= (len(word2_indices) + lbl_len)
            neu1e = train_cbow_pair(model, word, word2_indices, l1, alpha, neg_labels, train_words, train_words)
            if train_lbls:
                model.syn0[lbl_indices] += neu1e

        return len([word for word in sentence if word is not None])


class LabeledSentence(object):
    """
    A single labeled sentence = text item.
    Replaces "sentence as a list of words" from Word2Vec.

    """
    def __init__(self, words, labels):
        """
        `words` is a list of tokens (unicode strings), `labels` a
        list of text labels associated with this text.

        """
        self.words = words
        self.labels = labels

    def __str__(self):
        return '%s(%s, %s)' % (self.__class__.__name__, self.words, self.labels)


class Doc2Vec(Word2Vec):
    """Class for training, using and evaluating neural networks described in http://arxiv.org/pdf/1405.4053v2.pdf"""
    def __init__(self, sentences=None, size=300, alpha=0.025, window=8, min_count=5,
                 sample=0, seed=1, workers=1, min_alpha=0.0001, dm=1, hs=1, negative=0,
                 dm_mean=0, train_words=True, train_lbls=True, **kwargs):
        """
        Initialize the model from an iterable of `sentences`. Each sentence is a
        LabeledSentence object that will be used for training.

        The `sentences` iterable can be simply a list of LabeledSentence elements, but for larger corpora,
        consider an iterable that streams the sentences directly from disk/network.

        If you don't supply `sentences`, the model is left uninitialized -- use if
        you plan to initialize it in some other way.

        `dm` defines the training algorithm. By default (`dm=1`), distributed memory is used.
        Otherwise, `dbow` 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 zero as training progresses).

        `seed` = for the random number generator.

        `min_count` = ignore all words with total frequency lower than this.

        `sample` = threshold for configuring which higher-frequency words are randomly downsampled;
                default is 0 (off), useful value is 1e-5.

        `workers` = use this many worker threads to train the model (=faster training with multicore machines).

        `hs` = if 1 (default), hierarchical sampling will be used for model training (else set to 0).

        `negative` = if > 0, negative sampling will be used, the int for negative
        specifies how many "noise words" should be drawn (usually between 5-20).

        `dm_mean` = if 0 (default), use the sum of the context word vectors. If 1, use the mean.
        Only applies when dm is used.

        """
        Word2Vec.__init__(self, size=size, alpha=alpha, window=window, min_count=min_count,
                          sample=sample, seed=seed, workers=workers, min_alpha=min_alpha,
                          sg=(1+dm) % 2, hs=hs, negative=negative, cbow_mean=dm_mean, **kwargs)
        self.train_words = train_words
        self.train_lbls = train_lbls
        self.labels = set()
        if sentences is not None:
            self.build_vocab(sentences)
            self.train(sentences)
            self.build_labels(sentences)

    @staticmethod
    def _vocab_from(sentences):
        sentence_no, vocab = -1, {}
        total_words = 0
        for sentence_no, sentence in enumerate(sentences):
            if sentence_no % 10000 == 0:
                logger.info("PROGRESS: at item #%i, processed %i words and %i word types" %
                            (sentence_no, total_words, len(vocab)))
            sentence_length = len(sentence.words)
            for label in sentence.labels:
                total_words += 1
                if label in vocab:
                    vocab[label].count += sentence_length
                else:
                    vocab[label] = Vocab(count=sentence_length)
            for word in sentence.words:
                total_words += 1
                if word in vocab:
                    vocab[word].count += 1
                else:
                    vocab[word] = Vocab(count=1)
        logger.info("collected %i word types from a corpus of %i words and %i items" %
                    (len(vocab), total_words, sentence_no + 1))
        return vocab

    def _prepare_sentences(self, sentences):
        for sentence in sentences:
            # avoid calling random_sample() where prob >= 1, to speed things up a little:
            sampled = [self.vocab[word] for word in sentence.words
                       if word in self.vocab and (self.vocab[word].sample_probability >= 1.0 or
                                                  self.vocab[word].sample_probability >= random.random_sample())]
            yield (sampled, [self.vocab[word] for word in sentence.labels if word in self.vocab])

    def _get_job_words(self, alpha, work, job, neu1):
        if self.sg:
            return sum(train_sentence_dbow(self, sentence, lbls, alpha, work, self.train_words, self.train_lbls) for sentence, lbls in job)
        else:
            return sum(train_sentence_dm(self, sentence, lbls, alpha, work, neu1, self.train_words, self.train_lbls) for sentence, lbls in job)

    def __str__(self):
        return "Doc2Vec(vocab=%s, size=%s, alpha=%s)" % (len(self.index2word), self.layer1_size, self.alpha)

    def save(self, *args, **kwargs):
        kwargs['ignore'] = kwargs.get('ignore', ['syn0norm'])  # don't bother storing the cached normalized vectors
        super(Doc2Vec, self).save(*args, **kwargs)

    def build_labels(self, sentences):
        self.labels |= self._labels_from(sentences)

    @staticmethod
    def _labels_from(sentences):
        labels = set()
        for sentence in sentences:
            labels |= set(sentence.labels)
        return labels

    def most_similar_labels(self, positive=[], negative=[], topn=10):
        """
        Find the top-N most similar labels.
        """
        result = self.most_similar(positive=positive, negative=negative, topn=len(self.vocab))
        result = [(k, v) for (k, v) in result if k in self.labels]
        return result[:topn]

    def most_similar_words(self, positive=[], negative=[], topn=10):
        """
        Find the top-N most similar words.
        """
        result = self.most_similar(positive=positive, negative=negative, topn=len(self.vocab))
        result = [(k, v) for (k, v) in result if k not in self.labels]
        return result[:topn]

    def most_similar_vocab(self, positive=[], negative=[], vocab=[], topn=10, cosmul=False):
        """
        Find the top-N most similar words in vocab list.
        """
        if cosmul:
            result = self.most_similar_cosmul(positive=positive, negative=negative, topn=len(self.vocab))
        else:
            result = self.most_similar(positive=positive, negative=negative, topn=len(self.vocab))
        result = [(k, v) for (k, v) in result if k in vocab]
        return result[:topn]

class LabeledBrownCorpus(object):
    """Iterate over sentences from the Brown corpus (part of NLTK data), yielding
    each sentence out as a LabeledSentence object."""
    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 item_no, line in enumerate(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 LabeledSentence(words, ['%s_SENT_%s' % (fname, item_no)])


class LabeledLineSentence(object):
    """Simple format: one sentence = one line = one LabeledSentence object.

    Words are expected to be already preprocessed and separated by whitespace,
    labels are constructed automatically from the sentence line number."""
    def __init__(self, source):
        """
        `source` can be either a string (filename) or a file object.

        Example::

            sentences = LineSentence('myfile.txt')

        Or for compressed files::

            sentences = LineSentence('compressed_text.txt.bz2')
            sentences = LineSentence('compressed_text.txt.gz')

        """
        self.source = source

    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 item_no, line in enumerate(self.source):
                yield LabeledSentence(utils.to_unicode(line).split(), ['SENT_%s' % item_no])
        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 item_no, line in enumerate(fin):
                    yield LabeledSentence(utils.to_unicode(line).split(), ['SENT_%s' % item_no])

class LabeledListSentence(object):
    """one sentence = list of words

    labels are constructed automatically from the sentence line number."""
    def __init__(self, words_list, labels):
        """
        words_list like:

            words_list = [
                ['human', 'interface', 'computer'],
                ['survey', 'user', 'computer', 'system', 'response', 'time'],
                ['eps', 'user', 'interface', 'system'],
            ]
            sentence = LabeledListSentence(words_list)

        """
        self.words_list = words_list
        self.labels = labels

    def __iter__(self):
        for i, words in enumerate(self.words_list):
            yield LabeledSentence(words, ['SENT_%s' % self.labels[i]])

■ wikipediaのデータからコーパスを作成する。

※ここは省いても動きます。

wget http://dumps.wikimedia.org/jawiki/latest/jawiki-latest-pages-  articles.xml.bz2
#ダウンロードに10分くらいかかるかも
python path/to/wikicorpus.py path/to/jawiki-latest-pages-articles.xml.bz2 path/to/jawiki
#8時間くらいかかるかも

実装(実践)

実際のデータを読み込ませて、類似度やベクトル計算をしてみる。
今回は、ドキュメント(docs)とそのタイトル(titles)を読み込ませて、docsをベクトル化して類似度やベクトル計算をしてみました。

main.py
import gensim
import mysql.connector

#定義
previous_title = ""
docs = []
titles = []

#MySQLに接続
config = {
  'user': "USERNAME",
  'password': 'PASSWORD',
  'host': 'HOST',
  'database': 'DATABASE',
  'port': 'PORT'
}
connect = mysql.connector.connect(**config)
#Queryを実行する
cur=connect.cursor(buffered=True)

QUERY = "select d.title,d.body from docs as d order by doc.id" #ここはカスタマイズしてください
cur.execute(QUERY)
rows = cur.fetchall()

#Queryの出力結果をforで回してsentencesとlabelsを作成
i = 0
for row in rows:
  if previous_title != row[0]:
    previous_title = row[0]
    titles.append(row[0])
    docs.append([])
    i+=1
  docs[i-1].append(row[1])

cur.close()
connect.close()

"""
上で作っているデータは要するにこういうデータです。
docs = [
    ['human', 'interface', 'computer'], #0
    ['survey', 'user', 'computer', 'system', 'response', 'time'], #1
    ['eps', 'user', 'interface', 'system'], #2
    ['system', 'human', 'system', 'eps'], #3
    ['user', 'response', 'time'], #4
    ['trees'], #5
    ['graph', 'trees'], #6
    ['graph', 'minors', 'trees'], #7
    ['graph', 'minors', 'survey'] #8
]

titles = [
    "doc1",
    "doc2",
    "doc3",
    "doc4",
    "doc5",
    "doc6",
    "doc7",
    "doc8",
    "doc9"
]
"""

labeledSentences = gensim.models.doc2vec.LabeledListSentence(docs,titles)
model = gensim.models.doc2vec.Doc2Vec(labeledSentences, min_count=0)

# ある文書に似ている文書を表示
print model.most_similar_labels('SENT_doc1')

# ある文書に似ている単語を表示
print model.most_similar_words('SENT_doc1')

# 複数の文書を加算減算した上で、似ているユーザーを表示
print model.most_similar_labels(positive=['SENT_doc1', 'SENT_doc2'], negative=['SENT_doc3'], topn=5)

# 複数の文書を加算減算した上で、似ている単語を表示
print model.most_similar_words(positive=['SENT_doc1', 'SENT_doc2'], negative=['SENT_doc3'], topn=5)

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