AttributeError:“列表"对象没有属性“复制" [英] AttributeError: 'list' object has no attribute 'copy'

问题描述

我有以下代码段

classifier = NaiveBayesClassifier.train(train_data)
#classifier.show_most_informative_features(n=20)
results = classifier.classify(test_data)

，错误显示在下一行

results = classifier.classify(test_data)

错误:

Traceback (most recent call last):
  File "trial_trial.py", line 46, in <module>
    results = classifier.classify(test_data)
  File "c:\Users\Amr\Anaconda\lib\site-packages\nltk\classify\naivebayes.py", line 88, in classify
    return self.prob_classify(featureset).max()
  File "c:\Users\Amr\Anaconda\lib\site-packages\nltk\classify\naivebayes.py", line 94, in prob_classify
    featureset = featureset.copy()
AttributeError: 'list' object has no attribute 'copy'

我想到了在python中扩展基类列表并添加复制功能 但是我不是python专家，也不知道如何解决这个问题.

I think of extending base class list in python and add copy function but I'm not expert in python and I don't know how to solve this problem.

推荐答案

NLTK分类器与功能集一起使用；这些总是作为字典给出，其特征名称映射到一个值.相反，您传递的是列表，因此不会按照NLTK文档的要求生成功能.该代码只需要一个Python字典，而Python字典具有一个.copy()方法.

NLTK classifiers work with feature sets; these are always given as dictionaries with feature names mapping to a value. You are passing in a list instead, so you are not producing features as per the NLTK documentation. The code simply expects a Python dictionary, and Python dictionaries have a .copy() method.

请参阅关于学习文本分类 :

See the NLTK tutorial chapter on Learning to Classify Text:

返回的字典，称为功能集，从功能名称映射到其值.特征名称是区分大小写的字符串，通常提供对特征的简短人类可读描述，例如示例'last_letter'.特征值是具有简单类型的值，例如布尔值，数字和字符串.

The returned dictionary, known as a feature set, maps from feature names to their values. Feature names are case-sensitive strings that typically provide a short human-readable description of the feature, as in the example 'last_letter'. Feature values are values with simple types, such as booleans, numbers, and strings.

另请参阅NLTK分类的 功能集部分API文档:

Also see the Featuresets section of the NLTK Classify API documentation:

使用功能集"对描述令牌的功能进行编码，该功能集是从功能名称"映射到功能值"的字典.功能名称是唯一的字符串，用于指示功能对令牌的哪个方面进行编码.

The features describing a token are encoded using a "featureset", which is a dictionary that maps from "feature names" to "feature values". Feature names are unique strings that indicate what aspect of the token is encoded by the feature.

您尚未共享train_data列表包含的对象类型.如果这些是功能集词典，则您应该使用classify_many()代替:

You haven't shared what kind of objects the train_data list contains; if those are feature set dictionaries, you want to use classify_many() instead:

results = classifier.classify_many(test_data)

该方法确实带有一个列表，但是每个元素仍然必须是有效的功能集.

That method does take a list, but each element must still be a valid feature set.

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