10

我已经安装了 python 2.7、numpy 1.9.0、scipy 0.15.1 和 scikit-learn 0.15.2。现在，当我在 python 中执行以下操作时：

train_set = ("The sky is blue.", "The sun is bright.")
test_set = ("The sun in the sky is bright.",
"We can see the shining sun, the bright sun.")

from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()

print vectorizer


    CountVectorizer(analyzer=u'word', binary=False, charset=None,
    charset_error=None, decode_error=u'strict',
    dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
    lowercase=True, max_df=1.0, max_features=None, min_df=1,
    ngram_range=(1, 1), preprocessor=None, stop_words=None,
    strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
    tokenizer=None, vocabulary=None)

     vectorizer.fit_transform(train_set)
    print vectorizer.vocabulary

    None.

实际上它应该打印以下内容：

CountVectorizer(analyzer__min_n=1,
analyzer__stop_words=set(['all', 'six', 'less', 'being', 'indeed', 'over',    
 'move', 'anyway', 'four', 'not', 'own', 'through', 'yourselves', (...) --->     
For count vectorizer

{'blue': 0, 'sun': 1, 'bright': 2, 'sky': 3} ---> for vocabulary

以上代码来自博客： http ://blog.christianperone.com/?p=1589

你能帮我解释一下为什么会出现这样的错误。由于词汇表没有正确索引，我无法继续理解 TF-IDF 的概念。我是 python 的新手，所以任何帮助将不胜感激。

弧。

4

2 回答 2

20

您缺少下划线，请尝试以下方式：

from sklearn.feature_extraction.text import CountVectorizer
train_set = ("The sky is blue.", "The sun is bright.")
test_set = ("The sun in the sky is bright.", 
    "We can see the shining sun, the bright sun.")

vectorizer = CountVectorizer(stop_words='english')
document_term_matrix = vectorizer.fit_transform(train_set)
print vectorizer.vocabulary_
# {u'blue': 0, u'sun': 3, u'bright': 1, u'sky': 2}

如果你使用 ipython shell，你可以使用 tab 补全，你可以更容易地找到对象的方法和属性。

于 2015-03-06T09:00:08.380 回答
5

尝试使用该vectorizer.get_feature_names()方法。它按照出现在document_term_matrix.

from sklearn.feature_extraction.text import CountVectorizer
train_set = ("The sky is blue.", "The sun is bright.")
test_set = ("The sun in the sky is bright.", 
    "We can see the shining sun, the bright sun.")

vectorizer = CountVectorizer(stop_words='english')
document_term_matrix = vectorizer.fit_transform(train_set)
vectorizer.get_feature_names()
#> ['blue', 'bright', 'sky', 'sun']
于 2017-06-02T03:53:13.577 回答