计算字符串的 tf-idf

Question

我有 2 个文档 doc1.txt 和 doc2.txt。这两个文件的内容是：

 #doc1.txt
 very good, very bad, you are great

 #doc2.txt
 very bad, good restaurent, nice place to visit

我想让我的语料库与, 分开，这样我的最终DocumentTermMatrix 就变成了：

      terms
 docs       very good      very bad        you are great   good restaurent   nice place to visit
 doc1       tf-idf          tf-idf         tf-idf          0                    0
 doc2       0               tf-idf         0               tf-idf             tf-idf

我知道如何计算单个单词的DocumentTermMatrix（使用http://scikit-learn.org/stable/modules/feature_extraction.html），但不知道如何在Python 中计算strings 的DocumentTermMatrix。

Answer 1

您可以将TfidfVectorizer 的analyzer 参数指定为以自定义方式提取特征的函数：

from sklearn.feature_extraction.text import TfidfVectorizer

docs = ['very good, very bad, you are great',
        'very bad, good restaurent, nice place to visit']

tfidf = TfidfVectorizer(analyzer=lambda d: d.split(', ')).fit(docs)
print tfidf.get_feature_names()

得到的特征是：

['good restaurent', 'nice place to visit', 'very bad', 'very good', 'you are great']

---

如果您真的无法将所有数据加载到内存中，这是一种解决方法：

from sklearn.feature_extraction.text import TfidfVectorizer

docs = ['doc1.txt', 'doc2.txt']

def extract(filename):
    with open(filename) as f:
        features = []
        for line in f:
            features += line.strip().split(', ')
        return features

tfidf = TfidfVectorizer(analyzer=extract).fit(docs)
print tfidf.get_feature_names()

它一次加载每个文档，而不是一次将所有文档都保存在内存中。