CountVectorizer不打印词汇表

Question

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我会犯这样的错误吗。由于词汇索引不正确，我无法理解TF-IDF的概念。我是python的新手，所以任何帮助都将不胜感激。

弧形。

Answer 1

如果缺少下划线，请尝试以下方法：

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}

如果使用ipython shell，则可以使用制表符完成，并且可以更容易地找到对象的方法和属性。

Answer 2

尝试使用vectorizer.get_feature_names()方法。它按照在document_term_matrix中出现的顺序提供列名。

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']