自定义词汇表上的Sklearn Countvectorizer

Question

我有一组网页，并且我正在获取网页计数矩阵。我尝试使用sklearn的标准Countvectorizer，但未获得所需的结果。示例代码如下：

from sklearn.feature_extraction.text import CountVectorizer
corpus = ['www.google.com www.google.com', 'www.google.com www.facebook.com', 'www.google.com', 'www.facebook.com']
vocab = {'www.google.com':0, 'www.facebook.com':1}
vectorizer = CountVectorizer(vocabulary=vocab)
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray()) 

它给出

['www.google.com', 'www.facebook.com']
[[0 0]
 [0 0]
 [0 0]
 [0 0]]

但是所需的结果是

['www.google.com', 'www.facebook.com']
[[2 0]
 [1 1]
 [1 0]
 [0 1]]

如何在这样的自定义词汇表上应用countvectorizer？

Answer 1

As per the input from a related question, the issue occured because of the tokenizer. A customer tokenizer was written and now it works.

def mytokenizer(text):
    return text.split()

from sklearn.feature_extraction.text import CountVectorizer
corpus = ['www.google.com www.google.com', 'www.google.com www.facebook.com', 'www.google.com', 'www.facebook.com']
vocab = {'www.google.com':0, 'www.facebook.com':1}
vectorizer = CountVectorizer(vocabulary=vocab, tokenizer = mytokenizer)
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
print(X.toarray())