我通过保存成功保存了功能列表vectorizer.vocabulary_，并通过CountVectorizer(decode_error="replace",vocabulary=vectorizer.vocabulary_)
以下代码：
corpus = np.array(["aaa bbb ccc", "aaa bbb ddd"])
vectorizer = CountVectorizer(decode_error="replace")
vec_train = vectorizer.fit_transform(corpus)
#Save vectorizer.vocabulary_
pickle.dump(vectorizer.vocabulary_,open("feature.pkl","wb"))
#Load it later
transformer = TfidfTransformer()
loaded_vec = CountVectorizer(decode_error="replace",vocabulary=pickle.load(open("feature.pkl", "rb")))
tfidf = transformer.fit_transform(loaded_vec.fit_transform(np.array(["aaa ccc eee"])))
这样可行。tfidf具有与训练数据相同的特征长度。
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