如何在 Python 中将 Countvectorized 数据转换回文本数据

Question

如何将计数矢量化文本数据转换回文本形式。我有文本数据，我使用计数矢量器将其制作成稀疏矩阵进行分类。现在，我希望将文本数据的稀疏 martix 转换回文本数据。

我的代码

 cv = CountVectorizer( max_features = 500,analyzer='word') 
    cv_addr = cv.fit_transform(data.pop('Clean_addr'))
    for i, col in enumerate(cv.get_feature_names()):
        data[col] = pd.SparseSeries(cv_addr[:, i].toarray().ravel(), fill_value=0)

Answer 1

我认为

这是不可能的 - 所有标点符号、空格、制表符都已删除。此外，所有单词都已转换为小写。AFAIK 没有办法让它恢复为原始格式。因此，您最好保留Clean_addr列而不是删除它。

演示：

In [18]: df
Out[18]:
                                         txt
0                              a sample text
1  to be, or not to be, that is the question
In [19]: from sklearn.feature_extraction.text import CountVectorizer
In [20]: cv = CountVectorizer(max_features = 500, analyzer='word')
In [21]: cv_addr = cv.fit_transform(df['txt'])
In [22]: x = pd.SparseDataFrame(cv_addr, columns=cv.get_feature_names(), 
                                index=df.index, default_fill_value=0)
In [23]: x
Out[23]:
   be  is  not  or  question  sample  text  that  the  to
0   0   0    0   0         0       1     1     0    0   0
1   2   1    1   1         1       0     0     1    1   2
In [24]: df.join(x)
Out[24]:
                                         txt  be  is  not  or  question  sample  text  that  the  to
0                              a sample text   0   0    0   0         0       1     1     0    0   0
1  to be, or not to be, that is the question   2   1    1   1         1       0     0     1    1   2