尝试增加ngram_rangein TfidfVectorizer：
tfidf = TfidfVectorizer(vocabulary = myvocabulary, stop_words = 'english', ngram_range=(1,2))
的输出TfidfVectorizer是稀疏格式的TF-IDF矩阵（或者实际上是您寻求的格式的转置）。您可以打印出其内容，例如：
feature_names = tfidf.get_feature_names()
corpus_index = [n for n in corpus]
rows, cols = tfs.nonzero()
for row, col in zip(rows, cols):
print((feature_names[col], corpus_index[row]), tfs[row, col])
应该产生
('biscuit pudding', 1) 0.646128915046
('chocolates', 1) 0.763228291628
('chocolates', 2) 0.508542320378
('tim tam', 2) 0.861036995944
('chocolates', 3) 0.508542320378
('fresh milk', 3) 0.861036995944
如果矩阵不大，则以密集形式检查矩阵可能会更容易。Pandas使这个非常方便：
import pandas as pd
df = pd.DataFrame(tfs.T.todense(), index=feature_names, columns=corpus_index)
print(df)
这导致
1 2 3
tim tam 0.000000 0.861037 0.000000
jam 0.000000 0.000000 0.000000
fresh milk 0.000000 0.000000 0.861037
chocolates 0.763228 0.508542 0.508542
biscuit pudding 0.646129 0.000000 0.000000
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