Python gensim (TfidfModel)：Tf-Idf 是如何计算的？

Question

1.对于下面的测试文本，

test=['test test', 'test toy']

tf-idf 分数[没有归一化（smartirs：'ntn'）]是

[['test', 1.17]]  
[['test', 0.58], ['toy', 1.58]]

这似乎与我通过

直接计算得到的结果不符

tfidf (w, d) = tf x idf  
where idf(term)=log (total number of documents / number of documents containing term)   
tf = number of instances of word in d document / total number of words of d document

例

doc 1: 'test test'  
for "test" word  
tf= 1  
idf= log(2/2) = 0  
tf-idf = 0

谁能用我上面的测试文本告诉我计算结果？

2）当我改成余弦归一化（smartirs:'ntc'），我得到

[['test', 1.0]]  
[['test', 0.35], ['toy', 0.94]]

有人能告诉我计算结果吗？

谢谢

import gensim
from gensim import corpora
from gensim import models
import numpy as np
from gensim.utils import simple_preprocess

test=['test test', 'test toy']
 
texts = [simple_preprocess(doc) for doc in test]
 
mydict= corpora.Dictionary(texts)
mycorpus = [mydict.doc2bow(doc, allow_update=True) for doc in texts]
tfidf = models.TfidfModel(mycorpus, smartirs='ntn')
 
for doc in tfidf[mycorpus]:
    print([[mydict[id], np.around(freq, decimals=2)] for id, freq in doc])

Answer 1

如果您想知道 model.TfidfModel 的实施细节，您可以直接在 GitHub repository for gensim. The particular calculation scheme corresponding to smartirs='ntn' is described on the Wikipedia page for SMART Information Retrieval System 中查看它们，确切的计算与您使用的不同，因此在结果。

例如您所指的特定差异：

idf= log(2/2) = 0

实际上应该是 log2(N+1/n_k):

idf= log(2/1) = 1

我建议您检查实施和提到的页面，以确保您的手动检查遵循所选 smartirs 标志的实施。

Python gensim (TfidfModel)：Tf-Idf 是如何计算的？

Python gensim (TfidfModel): How is the Tf-Idf computed?

python

gensim

tf-idf