Gensim DOC2VEC 修剪和删除词汇表

Question

我尝试创建一个简单的 Doc2Vec 模型：

 sentences = []
 sentences.append(doc2vec.TaggedDocument(words=[u'scarpe', u'rosse', u'con', u'tacco'], tags=[1]))
 sentences.append(doc2vec.TaggedDocument(words=[u'scarpe', u'blu'], tags=[2]))
 sentences.append(doc2vec.TaggedDocument(words=[u'scarponcini', u'Emporio', u'Armani'], tags=[3]))
 sentences.append(doc2vec.TaggedDocument(words=[u'scarpe', u'marca', u'italiana'], tags=[4]))
 sentences.append(doc2vec.TaggedDocument(words=[u'scarpe', u'bianche', u'senza', u'tacco'], tags=[5]))

 model = Doc2Vec(alpha=0.025, min_alpha=0.025)  # use fixed learning rate
 model.build_vocab(sentences)

但我最终得到的是一个空词汇。通过一些调试，我看到在 build_vocab() 函数内部，字典实际上是由 vocabulary.scan_vocab() 函数创建的，但它被以下 vocabulary.prepare_vocab() 函数删除。更深入地说，这是导致问题的功能：

def keep_vocab_item(word, count, min_count, trim_rule=None):
    """Check that should we keep `word` in vocab or remove.

    Parameters
    ----------
    word : str
        Input word.
    count : int
        Number of times that word contains in corpus.
    min_count : int
        Frequency threshold for `word`.
    trim_rule : function, optional
        Function for trimming entities from vocab, default behaviour is `vocab[w] <= min_reduce`.

    Returns
    -------
    bool
        True if `word` should stay, False otherwise.

    """
    default_res = count >= min_count

    if trim_rule is None:
        return default_res # <-- ALWAYS RETURNS FALSE
    else:
        rule_res = trim_rule(word, count, min_count)
        if rule_res == RULE_KEEP:
            return True
        elif rule_res == RULE_DISCARD:
            return False
        else:
            return default_res

有人明白这个问题吗？

Answer 1

我自己找到了答案，min_count 的默认值是 5，我没有用 5 或更多的计数器的话。我只需要更改这行代码：

model = Doc2Vec(min_count=0, alpha=0.025, min_alpha=0.025)

Gensim DOC2VEC 修剪和删除词汇表

Gensim DOC2VEC trims and delete the vocabulary

python

gensim

doc2vec

vocabulary