Python 与gensim.models.Phrases有关的问题

现在是两种方法中的一种：
(一)

上面的运行非常好，没有任何警告

(二)

生成警告：

bigram_transformer = Phrases(SentenceClass(data_dir_path), min_count=1)
model = Word2Vec(bigram_transformer[SentenceClass(data_dir_path)], size=100, window=5, min_count=1, workers=4)

现在我确实理解了生成器和迭代器之间的区别，我正在传递迭代器，通过多次打印以下命令可以验证这一点：

WARNING:gensim.models.word2vec:train() called with an empty iterator (if not intended, be sure to provide a corpus that offers restartable iteration = an iterable).
WARNING:gensim.models.word2vec:supplied example count (0) did not equal expected count (30)

---

它打印的东西很好，但我仍然不确定为什么第二种情况下出现“空迭代器”的警告，我在这里遗漏了什么吗

我意识到短语和短语都只是一个生成器，下面的课程是必需的

print(list(SentenceClass(data_dir_path)))
print(list(SentenceClass(data_dir_path)))
print(list(bigram_transformer[SentenceClass(data_dir_path)]))
print(list(bigram_transformer[SentenceClass(data_dir_path)]))

---

我意识到短语和短语都只是发电机，下面的课程是必需的

print(list(SentenceClass(data_dir_path)))
print(list(SentenceClass(data_dir_path)))
print(list(bigram_transformer[SentenceClass(data_dir_path)]))
print(list(bigram_transformer[SentenceClass(data_dir_path)]))

print(list(SentenceClass(data_dir_path)))
print(list(SentenceClass(data_dir_path)))
print(list(bigram_transformer[SentenceClass(data_dir_path)]))
print(list(bigram_transformer[SentenceClass(data_dir_path)]))

from gensim.models import Word2Vec, Phrases, phrases

class PhraseItertor(object):

    def __init__(self, my_phraser, data):
        self.my_phraser, self.data = my_phraser, data

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

my_sentences = SentenceClass(data_dir_path)
my_phrases = Phrases(my_sentences, min_count=1)
bigram = phrases.Phraser(my_phrases)
my_corpus = PhraseItertor(bigram,my_sentences)

model = Word2Vec(my_corpus, size=100, window=5, min_count=1, workers=4)