我正在尝试运行此nltk网站提供的决策树代码。
http://www.nltk.org/howto/classify.html
>>> train = [
... (dict(a=1,b=1,c=1), 'y'),
... (dict(a=1,b=1,c=1), 'x'),
... (dict(a=1,b=1,c=0), 'y'),
... (dict(a=0,b=1,c=1), 'x'),
... (dict(a=0,b=1,c=1), 'y'),
... (dict(a=0,b=0,c=1), 'y'),
... (dict(a=0,b=1,c=0), 'x'),
... (dict(a=0,b=0,c=0), 'x'),
... (dict(a=0,b=1,c=1), 'y'),
... ]
>>>
>>>
>>> test = [
... (dict(a=1,b=0,c=1)), # unseen
... (dict(a=1,b=0,c=0)), # unseen
... (dict(a=0,b=1,c=1)), # seen 3 times, labels=y,y,x
... (dict(a=0,b=1,c=0)), # seen 1 time, label=x
... ]
>>>
>>>
>>> import nltk
>>> classifier = nltk.classify.DecisionTreeClassifier.train(train, entropy_cutoff=0, support_cutoff=0)
>>> sorted(classifier.labels())
['x', 'y']
>>> print(classifier)
c=0? .................................................. x
a=0? ................................................ x
a=1? ................................................ y
c=1? .................................................. y
>>> classifier.batch_classify(test)
['y', 'y', 'y', 'x']
>>> for pdist in classifier.batch_prob_classify(test):
... print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "//anaconda/lib/python2.7/site-packages/nltk/classify/api.py", line 87, in batch_prob_classify
return [self.prob_classify(fs) for fs in featuresets]
File "//anaconda/lib/python2.7/site-packages/nltk/classify/api.py", line 67, in prob_classify
raise NotImplementedError()
NotImplementedError
>>>
问题在于batch_prob_classify函数。请问有没有人能提供解决方法以及如何获取概率分布值。
DecisionTreeClassifier
类:DecisionTreeClassifier2(nltk.classify.DecisionTreeClassifier): . . .
。然后,您将使用典型的Python子类化策略添加feature_probdist
和label_probdist
。您可能需要覆盖此处的train
方法,并编写一个还包括此训练方法功能的方法。 - Justin O Barber