有没有办法在决策树的每个叶子下面获取样本？

1 回答

• 6

  如果您只想要每个样品的叶子，您可以使用

  clf.apply(iris.data)
  

  数组（[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1， 1,1,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,14,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,14,5,5 5,5,5,5,10,5,5,5,5,10,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,10,5,5,5,5,5,5,5,5,5,5,5,5,5,5 5,5,16,16,16,16,16,16,6,16,16,16,16,16,16,16,16,16,16,16,16,8,16,16,16， 16,16,16,15,16,16,11,16,16,16,8,8,16,16,16,15,16,16,16,16,16,16,16,16,16， 16,16]）

  如果要获取每个节点的所有样本，可以使用计算所有决策路径

  dec_paths = clf.decision_path(iris.data)
  

  然后遍历决策路径，将它们转换为带有 toarray() 的数组，并检查它们是否属于某个节点 . 所有内容都存储在 defaultdict 中，其中键是节点编号，值是样本编号 .

  for d, dec in enumerate(dec_paths):
      for i in range(clf.tree_.node_count):
          if dec.toarray()[0][i]  == 1:
              samples[i].append(d)
  

  Complete code

  import sklearn.datasets
  import sklearn.tree
  import collections
  
  clf = sklearn.tree.DecisionTreeClassifier(random_state=42)
  iris = sklearn.datasets.load_iris()
  clf = clf.fit(iris.data, iris.target)
  
  samples = collections.defaultdict(list)
  dec_paths = clf.decision_path(iris.data)
  
  for d, dec in enumerate(dec_paths):
      for i in range(clf.tree_.node_count):
          if dec.toarray()[0][i]  == 1:
              samples[i].append(d)
  

  Output

  print(samples[13])
  

  [70,126,138]

  回复于 2024-05-19T04:06:25+08:00