Question

我有一个代码可以计算150个文档的TF-IDF矩阵。

import re
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
all_lines = []
all_lines_corrected = []
with open("Extracted Functional Goals - Stemmed.txt") as f:
    for line in f:
        temp = line.split(None,1)
        all_lines.append(temp[1])


f.close()
for a in range(len(all_lines)-1):
    all_lines_corrected.append(all_lines[a][:-2])

all_lines_corrected.append(all_lines[len(all_lines)-1])

stop_words = stopwords.words('english')
tf = TfidfVectorizer(analyzer='word', stop_words = stop_words)
tfidf_matrix =  tf.fit_transform(all_lines_corrected).todense()
query_string = raw_input("Enter string : ")

如何获取查询字符串的TF-IDF？（我们可以假设它看起来像150个训练文件的入口吗？）

Answer 1

您可以使用values = tf.transform([query_string])获取查询字符串的tf-idf值。结果将是sparse matrix，其中包含1行和N列，其中列是矢量化程序在培训文档中看到的N个唯一字的tfidf值。

简短示例，类似于您的代码：

from sklearn.feature_extraction.text import TfidfVectorizer
all_lines = ["This is an example doc", "Another short example document .", "Just a third example"]

tf = TfidfVectorizer(analyzer='word')
tfidf_matrix =  tf.fit_transform(all_lines)
query_string = "This is a short example string"
print "Query String:"
print tf.transform([query_string])
print "Example doc:"
print tf.transform(["This is a short example doc"])

输出：

Query String:
  (0, 9)        0.546454011634
  (0, 7)        0.546454011634
  (0, 5)        0.546454011634
  (0, 4)        0.32274454218
Example doc:
  (0, 9)        0.479527938029
  (0, 7)        0.479527938029
  (0, 5)        0.479527938029
  (0, 4)        0.283216924987
  (0, 2)        0.479527938029

在训练的文档集上计算查询字符串的TF-IDF

1 个答案: