'Learn vocabulary and idf, return term-document matrix.
This is equivalent to fit followed by transform, but more efficiently
implemented.
Parameters
raw_documents : iterable
an iterable which yields either str, unicode or file objects
Returns
X : sparse matrix, [n_samples, n_features]
Tf-idf-weighted document-term matrix.'

def fit_transform(self, raw_documents, y=None):

    X = super(TfidfVectorizer, self).fit_transform(raw_documents)
    self._tfidf.fit(X)
    return self._tfidf.transform(X, copy=False)
