python 决策树规则提取_如何从scikit学习决策树中提取决策规则？

我创建了自己的函数，从sklearn创建的决策树中提取规则：import pandas as pd

import numpy as np

from sklearn.tree import DecisionTreeClassifier

# dummy data:

df = pd.DataFrame({'col1':[0,1,2,3],'col2':[3,4,5,6],'dv':[0,1,0,1]})

# create decision tree

dt = DecisionTreeClassifier(max_depth=5, min_samples_leaf=1)

dt.fit(df.ix[:,:2], df.dv)

此函数首先从节点(在子数组中由-1标识)开始，然后递归地查找父节点。我称之为节点的“血统”。一路上，我获取创建if/then/else SAS逻辑所需的值：def get_lineage(tree, feature_names):

left = tree.tree_.children_left

right = tree.tree_.children_right

threshold = tree.tree_.threshold

features = [feature_names[i] for i in tree.tree_.feature]

# get ids of child nodes

idx = np.argwhere(left == -1)[:,0]

def recurse(left, right, child, lineage=None):

if lineage is None: