使用sklearn为python中的n-gram计算TF-IDF

Question

我有一个包含n-gram的词汇表,如下所示.

myvocabulary = ['tim tam', 'jam', 'fresh milk', 'chocolates', 'biscuit pudding']

我想用这些词来计算TF-IDF值.

我还有一个语料库字典如下(键=食谱号,值=食谱).

corpus = {1: "making chocolates biscuit pudding easy first get your favourite biscuit chocolates", 2: "tim tam drink new recipe that yummy and tasty more thicker than typical milkshake that uses normal chocolates", 3: "making chocolates drink different way using fresh milk egg"}

我目前正在使用以下代码.

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf = TfidfVectorizer(vocabulary = myvocabulary, stop_words = 'english')
tfs = tfidf.fit_transform(corpus.values())

现在我正在打印令牌或n-gram的配方1 corpus以及tF-IDF值,如下所示.

feature_names = tfidf.get_feature_names()
doc = 0
feature_index = tfs[doc,:].nonzero()[1]
tfidf_scores = zip(feature_index, [tfs[doc, x] for x in feature_index])
for w, s in [(feature_names[i], s) for (i, s) in tfidf_scores]:
  print(w, s)

我得到的结果是chocolates 1.0.但是,我的代码不会检测n-gram(bigrams),例如biscuit pudding在计算TF-IDF值时.请让我知道我在哪里编写错误代码.

我想myvocabulary通过使用中的配方文档获得TD-IDF矩阵的术语corpus.换句话说,矩阵的行代表myvocabulary,矩阵的列代表我的食谱文档corpus.请帮我.

Answer 1

尝试增加ngram_rangein TfidfVectorizer:

tfidf = TfidfVectorizer(vocabulary = myvocabulary, stop_words = 'english', ngram_range=(1,2))

编辑:输出TfidfVectorizer是稀疏格式的TF-IDF矩阵(或实际上是你所寻找的格式的转置).你可以打印出它的内容,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])

哪个应该屈服

('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,则可能更容易以密集形式检查它.Pandas使这非常方便:

import pandas as pd
df = pd.DataFrame(tfs.T.todense(), index=feature_names, columns=corpus_index)
print(df)

这导致了

                        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