Question

我有两个目录，我想从中读取文本文件并标记它们，但我不知道如何通过TaggedDocument执行此操作。我认为它可以用作TaggedDocument（[Strings]，[Labels]），但这显然不起作用。

这是我的代码：

from gensim import models
from gensim.models.doc2vec import TaggedDocument
import utilities as util
import os
from sklearn import svm
from nltk.tokenize import sent_tokenize
CogPath = "./FixedCog/"
NotCogPath = "./FixedNotCog/"
SamplePath ="./Sample/"
docs = []
tags = []
CogList = [p for p in os.listdir(CogPath) if p.endswith('.txt')]
NotCogList = [p for p in os.listdir(NotCogPath) if p.endswith('.txt')]
SampleList = [p for p in os.listdir(SamplePath) if p.endswith('.txt')]
for doc in CogList:
     str = open(CogPath+doc,'r').read().decode("utf-8")
     docs.append(str)
     print docs
     tags.append(doc)
     print "###########"
     print tags
     print "!!!!!!!!!!!"
for doc in NotCogList:
     str = open(NotCogPath+doc,'r').read().decode("utf-8")
     docs.append(str)
     tags.append(doc)
for doc in SampleList:
     str = open(SamplePath + doc, 'r').read().decode("utf-8")
     docs.append(str)
     tags.append(doc)

T = TaggedDocument(docs,tags)

model = models.Doc2Vec(T,alpha=.025, min_alpha=.025, min_count=1,size=50)

这是我得到的错误：

Traceback (most recent call last):
  File "/home/farhood/PycharmProjects/word2vec_prj/doc2vec.py", line 34, in <module>
    model = models.Doc2Vec(T,alpha=.025, min_alpha=.025, min_count=1,size=50)
  File "/home/farhood/anaconda2/lib/python2.7/site-packages/gensim/models/doc2vec.py", line 635, in __init__
    self.build_vocab(documents, trim_rule=trim_rule)
  File "/home/farhood/anaconda2/lib/python2.7/site-packages/gensim/models/word2vec.py", line 544, in build_vocab
    self.scan_vocab(sentences, progress_per=progress_per, trim_rule=trim_rule)  # initial survey
  File "/home/farhood/anaconda2/lib/python2.7/site-packages/gensim/models/doc2vec.py", line 674, in scan_vocab
    if isinstance(document.words, string_types):
AttributeError: 'list' object has no attribute 'words'

Answer 1

所以我只是尝试了一下，在github上发现了这个：

class TaggedDocument(namedtuple('TaggedDocument', 'words tags')):
    """
    A single document, made up of `words` (a list of unicode string tokens)
    and `tags` (a list of tokens). Tags may be one or more unicode string
    tokens, but typical practice (which will also be most memory-efficient) is
    for the tags list to include a unique integer id as the only tag.

    Replaces "sentence as a list of words" from Word2Vec.

所以我决定通过为每个文档生成TaggedDocument类来改变我使用TaggedDocument函数的方式，重要的是你必须将标记作为列表传递。

for doc in CogList:
     str = open(CogPath+doc,'r').read().decode("utf-8")
     str_list = str.split()
     T = TaggedDocument(str_list,[doc])
     docs.append(T)

Answer 2

Doc2Vec模型的输入应为TaggedDocument（['list'，'of'，'word']，[TAG_001]）的列表。一个好的做法是将句子的索引用作标签。例如，要训练带有两个句子（即文档，段落）的Doc2Vec模型：

s1 = 'the quick fox brown fox jumps over the lazy dog'
s1_tag = '001'
s2 = 'i want to burn a zero-day'
s2_tag = '002'

docs = []
docs.append(TaggedDocument(words=s1.split(), tags=[s1_tag])
docs.append(TaggedDocument(words=s2.split(), tags=[s2_tag])

model = gensim.models.Doc2Vec(vector_size=300, window=5, min_count=5, workers=4, epochs=20)
model.build_vocab(docs)

print 'Start training process...'
model.train(docs, total_examples=model.corpus_count, epochs=model.iter)

#save model
model.save(model_path)

Answer 3

您可以使用gensim的common_texts为例：

from gensim.test.utils import common_texts
from gensim.models.doc2vec import Doc2Vec, TaggedDocument

documents = [TaggedDocument(doc, [i]) for i, doc in enumerate(common_texts)]
model = Doc2Vec(documents, vector_size=5, window=2, min_count=1, workers=4)

这将使用common_texts和TaggedDocument创建Doc2Vec算法期望的文档表示形式。

如何在gensim中使用TaggedDocument？

3 个答案: