使用来自sklearn.feature_extraction.text.TfidfVectorizer的TfidfVectorizer计算IDF

Question

我认为函数TfidfVectorizer不能正确计算IDF因子。 例如，复制从tf-idf feature weights using sklearn.feature_extraction.text.TfidfVectorizer代码：

from sklearn.feature_extraction.text import TfidfVectorizer 
corpus = ["This is very strange", 
      "This is very nice"] 
vectorizer = TfidfVectorizer(
         use_idf=True, # utiliza o idf como peso, fazendo tf*idf 
         norm=None, # normaliza os vetores 
         smooth_idf=False, #soma 1 ao N e ao ni => idf = ln(N+1/ni+1) 
         sublinear_tf=False, #tf = 1+ln(tf) 
         binary=False, 
         min_df=1, max_df=1.0, max_features=None, 
         strip_accents='unicode', # retira os acentos 
         ngram_range=(1,1), preprocessor=None,    stop_words=None, tokenizer=None, vocabulary=None 
      ) 
X = vectorizer.fit_transform(corpus) 
idf = vectorizer.idf_ 
print dict(zip(vectorizer.get_feature_names(), idf)) 

输出为：

{u'is': 1.0, 
u'nice': 1.6931471805599454, 
u'strange': 1.6931471805599454, 
u'this': 1.0, 
u'very': 1.0}` 

但应该是：

{u'is': 0.0, 
u'nice': 0.6931471805599454, 
u'strange': 0.6931471805599454, 
u'this': 0.0, 
u'very': 0.0} 

不是吗？我究竟做错了什么？

鉴于IDF的计算中，根据http://www.tfidf.com/，是：

IDF(t) = log_e(Total number of documents/Number of documents with term t in it) 

因此，当术语 '这个'， '是' 和 '非常' 出现在两个句子，的IDF = log_e（2/2）= 0.

术语'奇怪'和'好'只出现在两个文档中的一个中，所以log_e（2/1）= 0,69314。

Answer 1

两个事情发生，你可能不会在sklearn执行力度预计：

1. 的TfidfTransformer具有smooth_idf=True作为默认PARAM
2. 它总是加1重量

所以正在使用：

idf = log(1 + samples/documents) + 1 

这是源：

https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L987-L992

编辑： 你可以继承的标准TfidfVectorizer类是这样的：

import scipy.sparse as sp 
import numpy as np 
from sklearn.feature_extraction.text import (TfidfVectorizer, 
              _document_frequency) 
class PriscillasTfidfVectorizer(TfidfVectorizer): 

    def fit(self, X, y=None): 
     """Learn the idf vector (global term weights) 
     Parameters 
     ---------- 
     X : sparse matrix, [n_samples, n_features] 
      a matrix of term/token counts 
     """ 
     if not sp.issparse(X): 
      X = sp.csc_matrix(X) 
     if self.use_idf: 
      n_samples, n_features = X.shape 
      df = _document_frequency(X) 

      # perform idf smoothing if required 
      df += int(self.smooth_idf) 
      n_samples += int(self.smooth_idf) 

      # log+1 instead of log makes sure terms with zero idf don't get 
      # suppressed entirely. 
      ####### + 1 is commented out ########################## 
      idf = np.log(float(n_samples)/df) #+ 1.0 
      ####################################################### 
      self._idf_diag = sp.spdiags(idf, 
             diags=0, m=n_features, n=n_features) 

     return self 

Answer 2

他们在计算IDF实际使用的公式（当smooth_idf是真）是

idf = log((1 + samples)/(documents + 1)) + 1 

它在源代码中，但Web文档有点含糊不清hink。

https://github.com/scikit-learn/scikit-learn/blob/14031f6/sklearn/feature_extraction/text.py#L966-L969