文本处理 - 词组检测后的 Word2Vec 训练（二元模型）

Question

我想制作一个 word2vec 模型，其中包含比平常更多的 n-gram。正如我发现的那样，gensim.models.phrase 中的短语 class 可以找到我想要的短语，并且可以在语料库上使用短语并将其结果模型用于 word2vec 训练函数。

所以首先我会做一些类似下面的事情，就像 gensim documentation.

中的示例代码一样

class MySentences(object):
    def __init__(self, dirname):
        self.dirname = dirname

    def __iter__(self):
        for fname in os.listdir(self.dirname):
            for line in open(os.path.join(self.dirname, fname)):
                yield word_tokenize(line)

sentences = MySentences('sentences_directory')

bigram = gensim.models.Phrases(sentences)

model = gensim.models.Word2Vec(bigram['sentences'], size=300, window=5, workers=8)

模型已创建，但没有任何良好的评估结果和警告：

WARNING : train() called with an empty iterator (if not intended, be sure to provide a corpus that offers restartable iteration = an iterable)

我搜索了一下，找到了 https://groups.google.com/forum/#!topic/gensim/XWQ8fPMFSi0 并更改了我的代码：

class MySentences(object):
    def __init__(self, dirname):
        self.dirname = dirname

    def __iter__(self):
        for fname in os.listdir(self.dirname):
            for line in open(os.path.join(self.dirname, fname)):
                yield word_tokenize(line)

class PhraseItertor(object):
    def __init__(self, my_phraser, data):
        self.my_phraser, self.data = my_phraser, data

    def __iter__(self):
        yield self.my_phraser[self.data]


sentences = MySentences('sentences_directory')

bigram_transformer = gensim.models.Phrases(sentences)

bigram = gensim.models.phrases.Phraser(bigram_transformer)

corpus = PhraseItertor(bigram, sentences)

model = gensim.models.Word2Vec(corpus, size=300, window=5, workers=8)

我收到错误：

Traceback (most recent call last):
  File "/home/fatemeh/Desktop/Thesis/bigramModeler.py", line 36, in <module>
    model = gensim.models.Word2Vec(corpus, size=300, window=5, workers=8)
  File "/home/fatemeh/.local/lib/python3.4/site-packages/gensim/models/word2vec.py", line 478, in init
    self.build_vocab(sentences, trim_rule=trim_rule)
  File "/home/fatemeh/.local/lib/python3.4/site-packages/gensim/models/word2vec.py", line 553, in build_vocab
    self.scan_vocab(sentences, progress_per=progress_per, trim_rule=trim_rule)  # initial survey
  File "/home/fatemeh/.local/lib/python3.4/site-packages/gensim/models/word2vec.py", line 575, in scan_vocab
    vocab[word] += 1
TypeError: unhashable type: 'list'

现在我想知道我的代码有什么问题。

Answer 1

我在 Gensim GoogleGroup and Mr Gordon Mohr 中问了我的问题，回答了我：

You typically wouldn't want an __iter__() method to do a single yield. It should return an iterator object (ready to return multiple objects via next() or a StopIteration exception). One way to effect a iterator is to use yield to have the method treated as a 'generator' – but that would typically require the yield to be inside a loop.

But I now see that my example code in the thread you reference does the wrong thing with its __iter__() return line: it should not be returning the raw phrasifier, but one that has already been started-as-an-iterator, by use of the iter() built-in method. That is, the example there should have read:
class PhrasingIterable(object):
    def __init__(self, phrasifier, texts):
        self. phrasifier, self.texts = phrasifier, texts
    def __iter__():
        return iter(phrasifier[texts])
Making a similar change in your variation may resolve the TypeError: iter() returned non-iterator of type 'TransformedCorpus' error.

文本处理 - 词组检测后的 Word2Vec 训练（二元模型）

Text Processing - Word2Vec training after phrase detection (bigram model)

python

text-processing

gensim

word2vec