创建tf-idf值的矩阵

Question

我有一套documents如：

D1 = "The sky is blue."
D2 = "The sun is bright."
D3 = "The sun in the sky is bright."

和一组words如：

"sky","land","sea","water","sun","moon"

我想创建一个这样的矩阵：

   x        D1           D2         D3
sky         tf-idf       0          tf-idf
land        0            0          0
sea         0            0          0
water       0            0          0
sun         0            tf-idf     tf-idf
moon        0            0          0

类似于此处给出的示例表： http ： //www.cs.duke.edu/courses/spring14/compsci290/assignments/lab02.html 。 在给定的链接中，它使用文档中的相同单词，但我需要使用我提到的words集。

如果文档中存在特定单词，那么我将tf-idf值放入，否则我在矩阵中放置0 。

知道如何构建这样的矩阵吗？ Python将是最好的，但R也赞赏。

我使用以下代码但不确定我是否正在做正确的事情。 我的代码是：

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from nltk.corpus import stopwords


train_set = "The sky is blue.", "The sun is bright.", "The sun in the sky is bright." #Documents
test_set = ["sky","land","sea","water","sun","moon"] #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()

我得到了非常荒谬的结果（值只有0和1而我期望0到1之间的值）。

[[ 0.  0.  1.  0.]
 [ 0.  0.  0.  0.]
 [ 0.  0.  0.  0.]
 [ 0.  0.  0.  0.]
 [ 0.  0.  0.  1.]
 [ 0.  0.  0.  0.]
 [ 1.  0.  0.  0.]]   

我也对其他库开放计算tf-idf 。 我只想要一个我上面提到的正确矩阵。

Answer 1

AR解决方案可能如下所示：

library(tm)
docs <- c(D1 = "The sky is blue.",
          D2 = "The sun is bright.",
          D3 = "The sun in the sky is bright.")
dict <- c("sky","land","sea","water","sun","moon")
mat <- TermDocumentMatrix(Corpus(VectorSource(docs)), 
                          control=list(weighting =  weightTfIdf, 
                                       dictionary = dict))
as.matrix(mat)[dict, ]
#         Docs
# Terms          D1        D2        D3
#   sky   0.5849625 0.0000000 0.2924813
#   land  0.0000000 0.0000000 0.0000000
#   sea   0.0000000 0.0000000 0.0000000
#   water 0.0000000 0.0000000 0.0000000
#   sun   0.0000000 0.5849625 0.2924813
#   moon  0.0000000 0.0000000 0.0000000

Answer 2

我相信你想要的是

vectorizer = TfidfVectorizer(stop_words=stopWords, vocabulary=test_set)
matrix = vectorizer.fit_transform(train_set)

（正如我之前所说，这不是一个测试集，这是一个词汇表。）