我训练了scikit learn的TfidVectorizer的一个实例，并希望将其保存到磁盘。我将IDF矩阵（IDF_属性）作为numpy数组保存到磁盘，并将词汇表（词汇表_）作为JSON对象保存到磁盘（出于安全和其他原因，我避免pickle）。我正在尝试这样做：

import json
from idf import idf # numpy array with the pre-computed IDFs
from sklearn.feature_extraction.text import TfidfVectorizer

# dirty trick so I can plug my pre-computed IDFs
# necessary because "vectorizer.idf_ = idf" doesn't work,
# it returns "AttributeError: can't set attribute."
class MyVectorizer(TfidfVectorizer):
    TfidfVectorizer.idf_ = idf

# instantiate vectorizer
vectorizer = MyVectorizer(lowercase = False,
                          min_df = 2,
                          norm = 'l2',
                          smooth_idf = True)

# plug vocabulary
vocabulary = json.load(open('vocabulary.json', mode = 'rb'))
vectorizer.vocabulary_ = vocabulary

# test it
vectorizer.transform(['foo bar'])
Traceback (most recent call last):
  File "<stdin>", line 2, in <module>
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 1314, in transform
    return self._tfidf.transform(X, copy=False)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 1014, in transform
    check_is_fitted(self, '_idf_diag', 'idf vector is not fitted')
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/validation.py", line 627, in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.utils.validation.NotFittedError: idf vector is not fitted

那么，我做错了什么？我无法愚弄矢量器对象：不知何故，它知道我在作弊（即，传递它预先计算的数据，而不是用实际文本训练它）。我检查了矢量器对象的属性，但找不到类似“istrained”、“isfitted”之类的东西。那么，我如何愚弄矢量器呢？