问题
I tried to manually calculate tfidf values using the formula but the result I got is different from the result I got when using scikit-learn implementation.
from sklearn.feature_extraction.text import TfidfVectorizer
tv = TfidfVectorizer()
a = "cat hat bat splat cat bat hat mat cat"
b = "cat mat cat sat"
tv.fit_transform([a, b]).toarray()
# array([[0.53333448, 0.56920781, 0.53333448, 0.18973594, 0. ,
# 0.26666724],
# [0. , 0.75726441, 0. , 0.37863221, 0.53215436,
# 0. ]])
tv.get_feature_names()
# ['bat', 'cat', 'hat', 'mat', 'sat', 'splat']
I tried to manually calculate tfidf for document but result is different from TfidfVectorizer.fit_transform.
(np.log(2+1/1+1) + 1) * (2/9) = 0.5302876358044202
(np.log(2+1/2+1) + 1) * (3/9) = 0.750920989498456
(np.log(2+1/1+1) + 1) * (2/9) = 0.5302876358044202
(np.log(2+1/2+1) + 1) * (1/9) = 0.25030699649948535
(np.log(2+1/1+1) + 1) * (0/9) = 0.0
(np.log(2+1/1+1) + 1) * (1/9) = 0.2651438179022101
What I should have got is
[0.53333448, 0.56920781, 0.53333448, 0.18973594, 0, 0.26666724]
回答1:
There are many variations of TFIDF. The formula used by sklearn is:
(count_of_term_t_in_d) * ((log ((NUMBER_OF_DOCUMENTS + 1) / (Number_of_documents_where_t_appears +1 )) + 1)
2 * (np.log((1 + 2)/(1+1)) + 1) = 2.8109302162163288
3 * (np.log((1 + 2)/(2+1)) + 1) = 3.0
2 * (np.log((1 + 2)/(1+1)) + 1) = 2.8109302162163288
1 * (np.log((1 + 2)/(2+1)) + 1) = 1.0
0 * (np.log((1 + 2)/(2+1)) + 1) = 0.0
1 * (np.log((1 + 2)/(1+1)) + 1) = 1.4054651081081644
After the calculation, the final TFIDF vector is normalized by the Euclidean norm:
tfidf_vector = [2.8109302162163288, 3.0, 2.8109302162163288, 1.0, 0.0, 1.4054651081081644]
tfidf_vector = tfidf_vector / np.linalg.norm(tfidf_vector)
print(tfidf_vector)
[0.53333448, 0.56920781, 0.53333448, 0.18973594, 0, 0.26666724]
来源：https://stackoverflow.com/questions/54746926/scikit-learn-implementation-of-tfidf-differs-from-manual-implementation