AttributeError: 'CountVectorizer' object has no attribute 'get_feature_names_out'
时间: 2023-06-23 19:57:57 浏览: 152
这个错误通常是因为你使用了过时的代码或者版本不兼容的问题。在较新的版本中,`CountVectorizer`已经不再有`get_feature_names_out`方法。
如果你想获取特征名列表,可以使用`get_feature_names()`方法来代替。例如,你可以这样使用:
```
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
corpus = ["This is a sample sentence.", "This is another example sentence."]
X = vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names()
print(feature_names)
```
这样就可以获取到特征名列表。如果你还是遇到问题,可以尝试更新你的sklearn库或者查看文档来了解更多信息。
相关问题
AttributeError: 'CountVectorizer' object has no attribute 'get_feature_names'出错
`AttributeError: 'CountVectorizer' object has no attribute 'get_feature_names'`错误表示`CountVectorizer`对象没有`get_feature_names`属性。这个错误通常发生在使用`CountVectorizer`对象时,尝试调用`get_feature_names`方法时出错。
`get_feature_names`方法是用于获取`CountVectorizer`对象中的特征名称列表。如果你在使用`CountVectorizer`对象时遇到了这个错误,可能是因为你的对象没有正确初始化或者你的对象不是`CountVectorizer`类型。
以下是一些可能导致这个错误的原因和解决方法:
1. 检查你的代码中是否正确导入了`CountVectorizer`类。你可以使用`from sklearn.feature_extraction.text import CountVectorizer`来导入。
2. 确保你正确初始化了`CountVectorizer`对象。你可以使用`CountVectorizer()`来创建一个对象,并使用`fit_transform`方法来拟合和转换你的数据。
3. 确保你的对象是`CountVectorizer`类型。你可以使用`type(your_object)`来检查对象的类型。
以下是一个示例代码,演示了如何使用`CountVectorizer`对象并调用`get_feature_names`方法:
```python
from sklearn.feature_extraction.text import CountVectorizer
# 创建CountVectorizer对象
vectorizer = CountVectorizer()
# 拟合和转换数据
X = vectorizer.fit_transform(your_data)
# 获取特征名称列表
feature_names = vectorizer.get_feature_names()
print(feature_names)
```
AttributeError: 'CountVectorizer' object has no attribute 'get_feature_names'
This error occurs when you try to call the `get_feature_names` method on a `CountVectorizer` object, but the object does not have this attribute.
One possible reason for this error is that you have not fit the `CountVectorizer` object to your data yet. The `get_feature_names` method is only available after you have called the `fit_transform` or `fit` method on the `CountVectorizer` object.
Here is an example of how to use `CountVectorizer` and call `get_feature_names` method:
```
from sklearn.feature_extraction.text import CountVectorizer
# create a CountVectorizer object
vectorizer = CountVectorizer()
# fit the vectorizer to your data
X = ['this is a sample sentence', 'another example sentence']
vectorizer.fit_transform(X)
# get the feature names
feature_names = vectorizer.get_feature_names()
print(feature_names)
```
This should output a list of the unique words in your data:
```
['another', 'example', 'is', 'sample', 'sentence', 'this']
```