Question

我正在尝试使用Python的Tfidf来转换文本语料库。但是，当我尝试fit_transform它时，我得到一个值错误ValueError：空词汇;也许这些文件只包含停用词。

In [69]: TfidfVectorizer().fit_transform(smallcorp)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-69-ac16344f3129> in <module>()
----> 1 TfidfVectorizer().fit_transform(smallcorp)

/Users/maxsong/anaconda/lib/python2.7/site-packages/sklearn/feature_extraction/text.pyc in fit_transform(self, raw_documents, y)
   1217         vectors : array, [n_samples, n_features]
   1218         """
-> 1219         X = super(TfidfVectorizer, self).fit_transform(raw_documents)
   1220         self._tfidf.fit(X)
   1221         # X is already a transformed view of raw_documents so

/Users/maxsong/anaconda/lib/python2.7/site-packages/sklearn/feature_extraction/text.pyc in fit_transform(self, raw_documents, y)
    778         max_features = self.max_features
    779 
--> 780         vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary)
    781         X = X.tocsc()
    782 

/Users/maxsong/anaconda/lib/python2.7/site-packages/sklearn/feature_extraction/text.pyc in _count_vocab(self, raw_documents, fixed_vocab)
    725             vocabulary = dict(vocabulary)
    726             if not vocabulary:
--> 727                 raise ValueError("empty vocabulary; perhaps the documents only"
    728                                  " contain stop words")
    729 

ValueError: empty vocabulary; perhaps the documents only contain stop words

我在这里阅读了SO问题：Problems using a custom vocabulary for TfidfVectorizer scikit-learn并尝试了ogrisel建议使用 TfidfVectorizer（** params）.build_analyzer（）（dataset2）来检查文本分析步骤的结果这似乎按预期工作：下面的代码段：

In [68]: TfidfVectorizer().build_analyzer()(smallcorp)
Out[68]: 
[u'due',
 u'to',
 u'lack',
 u'of',
 u'personal',
 u'biggest',
 u'education',
 u'and',
 u'husband',
 u'to',

还有别的我做错了吗？我正在喂它的语料库只是一条由换行符打断的巨大长串。

谢谢！

Answer 1

我想这是因为你只有一个字符串。尝试将其拆分为字符串列表，例如：

In [51]: smallcorp
Out[51]: 'Ah! Now I have done Philosophy,\nI have finished Law and Medicine,\nAnd sadly even Theology:\nTaken fierce pains, from end to end.\nNow here I am, a fool for sure!\nNo wiser than I was before:'

In [52]: tf = TfidfVectorizer()

In [53]: tf.fit_transform(smallcorp.split('\n'))
Out[53]: 
<6x28 sparse matrix of type '<type 'numpy.float64'>'
    with 31 stored elements in Compressed Sparse Row format>

Answer 2

在版本0.12中，我们将最小文档频率设置为2，这意味着只会考虑至少出现两次的单词。要使您的示例正常工作，您需要设置min_df=1。从0.13开始，这是默认设置。所以我猜你使用的是0.12，对吧？

Answer 3

如果您坚持只有一个字符串，您也可以将单个字符串作为元组。而不是：

smallcorp = "your text"

你宁愿把它放在一个元组中。

In [22]: smallcorp = ("your text",)
In [23]: tf.fit_transform(smallcorp)
Out[23]: 
<1x2 sparse matrix of type '<type 'numpy.float64'>'
    with 2 stored elements in Compressed Sparse Row format>

Answer 4

在大型语料库上运行TF-IDF Python 3脚本时遇到了类似的错误。一些小文件（显然）缺少关键字，从而引发错误消息。

我尝试了几种无济于事的解决方案（如果将filtered添加到我的len(filtered = 0列表中，请输入伪字符串）。最简单的解决方案是添加一个try: ... except ... continue表达式。

pattern = "(?u)\\b[\\w-]+\\b"
cv = CountVectorizer(token_pattern=pattern)

# filtered is a list
filtered = [w for w in filtered if not w in my_stopwords and not w.isdigit()]

# ValueError:
# cv.fit(text)
# File "tfidf-sklearn.py", line 1675, in tfidf
#   cv.fit(filtered)
#   File "/home/victoria/venv/py37/lib/python3.7/site-packages/sklearn/feature_extraction/text.py", line 1024, in fit
#   self.fit_transform(raw_documents)
#   ...
#   ValueError: empty vocabulary; perhaps the documents only contain stop words

# Did not help:
# https://stackoverflow.com/a/20933883/1904943
#
# if len(filtered) == 0:
#     filtered = ['xxx', 'yyy', 'zzz']

# Solution:
try:
    cv.fit(filtered)
    cv.fit_transform(filtered)
    doc_freq_term_matrix = cv.transform(filtered)
except ValueError:
    continue

Answer 5

我也有同样的问题。将int（nums）列表转换为str（nums）列表没有帮助。但我转换为：

['d'+str(nums) for nums in set] #where d is some letter which mention, we work with strings

这有帮助。

Python TfidfVectorizer throw：空词汇;也许文件只包含停用词“

5 个答案: