我有一段代码可以计算150个文档的TF-IDF矩阵。

import refrom sklearn.feature_extraction.text import TfidfVectorizerfrom nltk.corpus import stopwordsall_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值？（我们可以假设它看起来像150个训练文档中的一个条目吗？）

回答：

你可以通过使用values = tf.transform([query_string])来获取查询字符串的tf-idf值。结果将是一个稀疏矩阵，具有1行和N列，其中列是向量化器在训练文档中见过的N个唯一词的tfidf值。

类似于你的代码的简短示例：

from sklearn.feature_extraction.text import TfidfVectorizerall_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)        0.32274454218Example doc:  (0, 9)        0.479527938029  (0, 7)        0.479527938029  (0, 5)        0.479527938029  (0, 4)        0.283216924987  (0, 2)        0.479527938029