CountVectorizer - 忽略出现少于 x 次且少于 y 个字符的单词

如何解决CountVectorizer - 忽略出现少于 x 次且少于 y 个字符的单词

我想知道是否有任何方法可以让 CountVectorizer() 忽略在所有文档中出现少于 x 次且少于 y 个字符的单词。类似于 wordlength 的 bounds (R) 中的 DocumentTermMatrix 和 tm 参数。

示例

这个语料库：

corpus = [
    'This is the first document.','This document is the second document.','And this is the third one.','Is this the first document?',]

现在变成这样：

>>> vectorizer = CountVectorizer()
>>> X = vectorizer.fit_transform(corpus)
>>> print(vectorizer.get_feature_names())
['and','document','first','is','one','second','the','third','this']
>>> print(X.toarray())
[[0 1 1 1 0 0 1 0 1]
 [0 2 0 1 0 1 1 0 1]
 [1 0 0 1 1 0 1 1 1]
 [0 1 1 1 0 0 1 0 1]]

将 x 和 y 设置为 2，我想要这样：

>>> vectorizer = CountVectorizer()
>>> X = vectorizer.fit_transform(corpus)
>>> print(vectorizer.get_feature_names())
['and','this']
>>> print(X.toarray())
[[1 1 1 1]
 [2 0 1 1]
 [0 0 1 1]
 [1 1 1 1]]

解决方法

您可能希望：

• 设置 min_df=2 来处理 x
• 定义 token_pattern=r"(?u)\b[a-zA-Z]{3,}\b" 来处理 y（您可以尝试 token_pattern=r"(?u)\b[a-zA-Z0-9_]{3,}\b" 在令牌定义中包含数字和下划线）

演示：

from sklearn.feature_extraction.text import CountVectorizer

corpus = [
    "This is the first document.","This document is the second document.","And this is the third one.","Is this the first document?",]

vectorizer = CountVectorizer(min_df=2,token_pattern=r"(?u)\b[a-zA-Z]{3,}\b")
X = vectorizer.fit_transform(corpus)
print(X.toarray())

---

[[1 1 1 1]
 [2 0 1 1]
 [0 0 1 1]
 [1 1 1 1]]