Python:tf-idf-cosine:查找文档相似性 [英] Python: tf-idf-cosine: to find document similarity

问题描述

我正在关注一个教程，该教程可在第1部分&中找到. 第2部分.不幸的是，作者没有时间进行最后一节，涉及使用余弦相似度实际找到两个文档之间的距离.我在 stackoverflow 中的以下链接的帮助下，遵循了本文中的示例.上面链接中提到的代码(只是为了使生活更轻松)

I was following a tutorial which was available at Part 1 & Part 2. Unfortunately the author didn't have the time for the final section which involved using cosine similarity to actually find the distance between two documents. I followed the examples in the article with the help of the following link from stackoverflow, included is the code mentioned in the above link (just so as to make life easier)

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import numpy as np
import numpy.linalg as LA

train_set = ["The sky is blue.", "The sun is bright."]  # Documents
test_set = ["The sun in the sky is bright."]  # Query
stopWords = stopwords.words('english')

vectorizer = CountVectorizer(stop_words = stopWords)
#print vectorizer
transformer = TfidfTransformer()
#print transformer

trainVectorizerArray = vectorizer.fit_transform(train_set).toarray()
testVectorizerArray = vectorizer.transform(test_set).toarray()
print 'Fit Vectorizer to train set', trainVectorizerArray
print 'Transform Vectorizer to test set', testVectorizerArray

transformer.fit(trainVectorizerArray)
print
print transformer.transform(trainVectorizerArray).toarray()

transformer.fit(testVectorizerArray)
print 
tfidf = transformer.transform(testVectorizerArray)
print tfidf.todense()

由于上述代码，我有以下矩阵

as a result of the above code I have the following matrix

Fit Vectorizer to train set [[1 0 1 0]
 [0 1 0 1]]
Transform Vectorizer to test set [[0 1 1 1]]

[[ 0.70710678  0.          0.70710678  0.        ]
 [ 0.          0.70710678  0.          0.70710678]]

[[ 0.          0.57735027  0.57735027  0.57735027]]

我不确定如何使用此输出来计算余弦相似度，我知道如何针对相似长度的两个向量实现余弦相似度，但是在这里我不确定如何识别这两个向量.

I am not sure how to use this output in order to calculate cosine similarity, I know how to implement cosine similarity with respect to two vectors of similar length but here I am not sure how to identify the two vectors.

推荐答案

借助@excray注释的帮助，我设法弄清楚了答案，实际上，我们需要编写一个简单的for循环来迭代分别代表火车数据和测试数据的两个数组.

WIth the Help of @excray's comment, I manage to figure it out the answer, What we need to do is actually write a simple for loop to iterate over the two arrays that represent the train data and test data.

首先实现一个简单的lambda函数来保存用于余弦计算的公式:

First implement a simple lambda function to hold formula for the cosine calculation:

cosine_function = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 3)

然后只需编写一个简单的for循环以遍历to向量，每个逻辑都是对于trainVectorizerArray中的每个向量，您必须在testVectorizerArray中找到与向量的余弦相似度."

And then just write a simple for loop to iterate over the to vector, logic is for every "For each vector in trainVectorizerArray, you have to find the cosine similarity with the vector in testVectorizerArray."

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords
import numpy as np
import numpy.linalg as LA

train_set = ["The sky is blue.", "The sun is bright."] #Documents
test_set = ["The sun in the sky is bright."] #Query
stopWords = stopwords.words('english')

vectorizer = CountVectorizer(stop_words = stopWords)
#print vectorizer
transformer = TfidfTransformer()
#print transformer

trainVectorizerArray = vectorizer.fit_transform(train_set).toarray()
testVectorizerArray = vectorizer.transform(test_set).toarray()
print 'Fit Vectorizer to train set', trainVectorizerArray
print 'Transform Vectorizer to test set', testVectorizerArray
cx = lambda a, b : round(np.inner(a, b)/(LA.norm(a)*LA.norm(b)), 3)

for vector in trainVectorizerArray:
    print vector
    for testV in testVectorizerArray:
        print testV
        cosine = cx(vector, testV)
        print cosine

transformer.fit(trainVectorizerArray)
print
print transformer.transform(trainVectorizerArray).toarray()

transformer.fit(testVectorizerArray)
print 
tfidf = transformer.transform(testVectorizerArray)
print tfidf.todense()

以下是输出:

Fit Vectorizer to train set [[1 0 1 0]
 [0 1 0 1]]
Transform Vectorizer to test set [[0 1 1 1]]
[1 0 1 0]
[0 1 1 1]
0.408
[0 1 0 1]
[0 1 1 1]
0.816

[[ 0.70710678  0.          0.70710678  0.        ]
 [ 0.          0.70710678  0.          0.70710678]]

[[ 0.          0.57735027  0.57735027  0.57735027]]

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