def test_pruning_non_leaf_parent(self):
a = LeafNode(Split(self.data))
b = LeafNode(Split(self.data))
c = LeafNode(Split(self.data))
d = DecisionNode(Split(self.data), a, b)
e = DecisionNode(Split(self.data), c, d)
with self.assertRaises(TypeError):
PruneMutation(e, a)
def test_head_prune(self):
b, c = LeafNode(Split(self.data)), LeafNode(Split(self.data))
a = DecisionNode(Split(self.data), b, c)
tree = Tree([a, b, c])
updated_a = LeafNode(Split(self.data))
prune_mutation = PruneMutation(a, updated_a)
mutate(tree, prune_mutation)
self.assertIn(updated_a, tree.leaf_nodes)
self.assertNotIn(self.a, tree.nodes)
def test_pruning_leaf(self):
with self.assertRaises(TypeError):
PruneMutation(LeafNode(Split(self.data)),
LeafNode(Split(self.data)))
def uniformly_sample_prune_mutation(tree: Tree) -> TreeMutation:
node = random_prunable_decision_node(tree)
updated_node = LeafNode(node.split, depth=node.depth)
return PruneMutation(node, updated_node)
def prune_mutations(tree: Tree) -> List[TreeMutation]:
return [
PruneMutation(x, LeafNode(x.split, depth=x.depth))
for x in tree.prunable_decision_nodes
]
def test_invalid_prune(self):
with self.assertRaises(TypeError):
updated_a = LeafNode(Split(self.data))
PruneMutation(self.a, updated_a)
