Getting decision path to a node in sklearn

Question

I wanted the decision path (i.e the set of rules) from the root node to a given node (which I supply) in a decision tree (DecisionTreeClassifier) in scikit-learn. clf.

Answer 1

If you supply None to the out_file in export_graphviz, you can get a string representation of the tree.

from sklearn.datasets import load_iris
from sklearn import tree

clf = tree.DecisionTreeClassifier()
iris = load_iris()

clf = clf.fit(iris.data, iris.target)
string_data = tree.export_graphviz(clf,
    out_file=None)

print(string_data)

#Output
digraph Tree {
node [shape=box] ;
0 [label="petal length (cm) <= 2.45\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]\nclass = setosa"] ;
1 [label="gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]\nclass = setosa"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="petal width (cm) <= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]\nclass = versicolor"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
3 [label="petal length (cm) <= 4.95\ngini = 0.168\nsamples = 54\nvalue = [0, 49, 5]\nclass = versicolor"] ;
2 -> 3 ;
4 [label="petal width (cm) <= 1.65\ngini = 0.041\nsamples = 48\nvalue = [0, 47, 1]\nclass = versicolor"] ;
3 -> 4 ;
5 [label="gini = 0.0\nsamples = 47\nvalue = [0, 47, 0]\nclass = versicolor"] ;
4 -> 5 ;
6 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]\nclass = virginica"] ;
4 -> 6 ;
7 [label="petal width (cm) <= 1.55\ngini = 0.444\nsamples = 6\nvalue = [0, 2, 4]\nclass = virginica"] ;
3 -> 7 ;
8 [label="gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]\nclass = virginica"] ;
7 -> 8 ;
9 [label="sepal length (cm) <= 6.95\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]\nclass = versicolor"] ;
7 -> 9 ;
10 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]\nclass = versicolor"] ;
9 -> 10 ;
11 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]\nclass = virginica"] ;
9 -> 11 ;
12 [label="petal length (cm) <= 4.85\ngini = 0.043\nsamples = 46\nvalue = [0, 1, 45]\nclass = virginica"] ;
2 -> 12 ;
13 [label="sepal length (cm) <= 5.95\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]\nclass = virginica"] ;
12 -> 13 ;
14 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]\nclass = versicolor"] ;
13 -> 14 ;
15 [label="gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]\nclass = virginica"] ;
13 -> 15 ;
16 [label="gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]\nclass = virginica"] ;
12 -> 16 ;
}

This will have what you want. You can then easily write a program to parse this to handle as you want.