Visualizations and analysis tools

Visualizing DecisionTrees

adaXT provides general plotting functionality with matplotlib. An example of plotting a DecisionTree with a maximum depth of 3 is shown below:

from adaXT.decision_tree import DecisionTree, plot_tree
import numpy as np
import matplotlib.pyplot as plt

N = 10000
M = 5
X = np.random.uniform(0, 100, (N, M))
Y = np.random.uniform(0, 4, N)
tree = DecisionTree("Regression", max_depth=3)
tree.fit(X, Y)

# Get screen width and height
dpi = plt.rcParams["figure.dpi"]


plt.figure(figsize=(19, 12))
plot_tree(tree)
plt.show()

The resulting plot will look similar to the following:

Analyzing the DecisionTree

adaXT DecisionTrees are built up of individual Nodes. Under the hood, adaXT calls these nodes when predicting. This makes it possible for the user to traverse the tree on their own, such that each individual node can be investigated in more detail. Below we provide an example script, which prints all the nodes in a tree.

from adaXT.decision_tree import DecisionNode, LeafNode
import numpy as np

def recurse_node_left(cur_node):
    if isinstance(cur_node, DecisionNode):
        print("DecisionNode")
        print(f"X{cur_node.split_idx} <= {cur_node.threshold}")
        recurse_node_left(cur_node.left_child)
        recurse_node_left(cur_node.right_child)
    else:
      # If not a DecisionNode, then it will always be LeafNode
      assert(isinstance(cur_node, LeafNode)) 
      print("LeafNode")
      print(f"Value: {cur_node.value}")


recurse_node_left(tree.root)

Applying this recursive function to the decision tree shown in the previous section, would result in the following output:

DecisionNode
X1 <= 22.623711878856856
DecisionNode
X1 <= 22.349316943202385
DecisionNode
X3 <= 97.66450448532115
LeafNode
Value: [1.92268088]
LeafNode
Value: [2.42121542]
DecisionNode
X2 <= 6.263581890564451
LeafNode
Value: [3.17642358]
LeafNode
Value: [1.0559612]
DecisionNode
X2 <= 50.61685788523325
DecisionNode
X1 <= 25.438273441628738
LeafNode
Value: [2.3826478]
LeafNode
Value: [2.03267368]
DecisionNode
X3 <= 15.113109194828294
LeafNode
Value: [2.09236662]
LeafNode
Value: [1.95213703]