如何在python中的sklearn中打印tf-idf得分矩阵

概述我使用sklearn获取tf-idf值如下. from sklearn.feature_extraction.text import TfidfVectorizermyvocabulary = ['life', 'learning']corpus = {1: "The game of life is a game of everlasting learning", 2: "The unexam 我使用sklearn获取tf-IDf值如下.

from sklearn.feature_extraction.text import TfIDfVectorizermyvocabulary = ['life','learning']corpus = {1: "The game of life is a game of everlasting learning",2: "The unexamined life is not worth living",3: "Never stop learning"}tfIDf = TfIDfVectorizer(vocabulary = myvocabulary,ngram_range = (1,3))tfs = tfIDf.fit_transform(corpus.values())

现在我想在矩阵中查看我计算的tf-IDf分数,如下所示.

我尝试按如下方式进行.

IDf = tfIDf.IDf_dic = dict(zip(tfIDf.get_feature_names(),IDf))print(dic)

但是,我得到如下输出.

{'life': 1.2876820724517808,'learning': 1.2876820724517808}

请帮我.

解决方法 感谢σηγ,我可以从 this question找到答案

feature_names = tfIDf.get_feature_names()corpus_index = [n for n in corpus]import pandas as pddf = pd.DataFrame(tfs.T.todense(),index=feature_names,columns=corpus_index)print(df)

总结

以上是内存溢出为你收集整理的如何在python中的sklearn中打印tf-idf得分矩阵全部内容，希望文章能够帮你解决如何在python中的sklearn中打印tf-idf得分矩阵所遇到的程序开发问题。

如果觉得内存溢出网站内容还不错，欢迎将内存溢出网站推荐给程序员好友。