'CountVectorizer' object has no attribute 'get_feature_names'
时间: 2023-10-03 22:05:49 浏览: 70
这个错误通常发生在使用Scikit-learn的CountVectorizer时没有正确的调用get_feature_names()方法。这个方法可以返回文本向量化后的特征名字。可能的原因是你没有正确地定义CountVectorizer或者没有正确地拟合模型。请确保你已经正确地定义了CountVectorizer并使用fit_transform()方法拟合了模型。然后,你可以通过调用get_feature_names()方法来检查特征名字是否正确。例如:
```
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
```
请确保你的代码中的变量名字和语料库正确。
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']
```