Question

我正在尝试运行以下nltk站点中提供的决策树代码 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
>>>

问题是batch_prob_classify函数的问题。任何人都可以建议如何解决问题以及如何获得概率分布值。

Answer 1

DecisionTreeClassifier使用概率类MLEProbDist，它没有任何prob方法。另一方面，NaiveBayesClassifier使用概率等级ELEProbDist，概率等级继承自LidstoneProbDist概率等级，做提供prob方法。

因此，除非您想创建DecisionTreeClassifier的子类并自行添加prob方法，否则您可能希望使用NaiveBayesClassifier代替：

>>> classifier = nltk.classify.NaiveBayesClassifier.train(train)  # note the use of NaiveBayesClassifier here
>>> for pdist in classifier.batch_prob_classify(test):
      print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))


0.3104 0.6896
0.5746 0.4254
0.3685 0.6315
0.6365 0.3635

正如@Mike指出的那样，你收到了预期的结果。您可能会对页面前面的一个非常类似的例子感到困惑。

Python决策树分类器batch_prob_classify函数

1 个答案: