问题描述
当我运行此代码时，它将引发错误，我认为是由于NLTK 3.0中不存在batch_classify方法所致.我很好奇如何解决这种问题，旧版本中的某些内容在新版本中消失了.
When I run this code it throws an error which I believe is due to the batch_classify method not being present in NLTK 3.0. I curious how to resolve this type of issue where something from an older version disappears in the newer version.
def accuracy(classifier, gold):
results = classifier.batch_classify([fs for (fs,l) in gold])
correct = [l==r for ((fs,l), r) in zip(gold, results)]
if correct:
return float(sum(correct))/len(correct)
else:
return 0
def apr (classifier, gold):
results = classifier.batch_classify([fs for (fs,l) in gold])
#gold_class_dict = defaultdict(list)
#classifier_class_dict = {}
sys_correct_dict = Counter()
num_guessed = Counter()
gold_num = Counter()
num_right = 0
total = 0
推荐答案
该方法已重命名为classify_many()(我找不到NLTK 2.0的文档进行检查，但我很确定那是发生了什么).
The method was renamed to classify_many() (I couldn't find documentation of NLTK 2.0 to check it, but I'm pretty sure that's what happened).
您必须在代码中用classify_many(...)替换所有出现的batch_classify(...).从库的一个主要版本迁移到另一个主要版本时，您必须期望这种向后不兼容的更改；理想情况下，应将它们记录在变更日志中.但是，我必须承认，过去NLTK甚至在次要版本之间都引入了向后不兼容的更改，我认为这是不好的做法.
You have to replace all occurrences of batch_classify(...) with classify_many(...) in your code.When moving from one major version of a library to another, you have to expect this kind of backwards-incompatible changes; they should ideally be documented in the changelog. However, I have to admit that in the past NLTK introduced backwards-incompatible changes even between minor versions, which I think is bad practice.
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