Gensim DOC2VEC trims and delete the vocabulary

Question

I tried creating a simple Doc2Vec model:

 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)  

But I end up with an empty vocabulary. With some debugging I saw that inside the build_vocab() function a dictionary is actually created by the vocabulary.scan_vocab() function, but it's being deleted by the following vocabulary.prepare_vocab() function. More deeply, this is the function that causes the problem:

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  

Does somebody understand the problem?

Answer 1

I found the answer myself, the default value for min_count is 5 and I had no words with a counter of 5 or more. I just had to change this line of code:

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