def setUp(self):
self.data = Data(pd.DataFrame({"a": [1]}).values, np.array([1]))
self.d = LeafNode(Split(self.data), None)
self.e = LeafNode(Split(self.data), None)
self.c = DecisionNode(Split(self.data), self.d, self.e)
self.b = LeafNode(Split(self.data))
self.a = DecisionNode(Split(self.data), self.b, self.c)
self.tree = Tree([self.a, self.b, self.c, self.d, self.e])
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_growing_decision_node(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):
GrowMutation(d, a)
def setUp(self):
self.data = Data(format_covariate_matrix(pd.DataFrame({"a": [1]})),
np.array([1]).astype(float))
self.d = LeafNode(Split(self.data), None)
self.e = LeafNode(Split(self.data), None)
self.c = DecisionNode(Split(self.data), self.d, self.e)
self.b = LeafNode(Split(self.data))
self.a = DecisionNode(Split(self.data), self.b, self.c)
self.tree = Tree([self.a, self.b, self.c, self.d, self.e])
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_grow(self):
f, g = LeafNode(Split(self.data)), LeafNode(Split(self.data))
updated_d = DecisionNode(Split(self.data), f, g)
grow_mutation = TreeMutation("grow", self.d, updated_d)
mutate(self.tree, grow_mutation)
self.assertIn(updated_d, self.tree.decision_nodes)
self.assertIn(updated_d, self.tree.prunable_decision_nodes)
self.assertIn(f, self.tree.leaf_nodes)
self.assertNotIn(self.d, self.tree.nodes)
def sample_split_node(node: LeafNode) -> DecisionNode:
"""
Split a leaf node into a decision node with two leaf children
The variable and value to split on is determined by sampling from their respective distributions
"""
if node.is_splittable():
conditions = sample_split_condition(node)
return split_node(node, conditions)
else:
return DecisionNode(node.split,
LeafNode(node.split, depth=node.depth + 1),
LeafNode(node.split, depth=node.depth + 1),
depth=node.depth)
def __init__(self, existing_node: DecisionNode, updated_node: LeafNode):
if not type(existing_node
) == DecisionNode or not existing_node.is_prunable():
raise TypeError("Pruning only valid on prunable decision nodes")
super().__init__("prune", existing_node, updated_node)