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
"""
conditions = sample_split_condition(node)
return split_node(node, conditions)
def search(index: int = 0):
left_child_index, right_child_index = sklearn_tree.children_left[
index], sklearn_tree.children_right[index]
if left_child_index == -1: # Trees are binary splits, so only need to check left tree
return
searched_node: LeafNode = nodes[index]
split_conditions = map_sklearn_split_into_bartpy_split_conditions(
sklearn_tree, index)
decision_node = split_node(searched_node, split_conditions)
left_child: LeafNode = decision_node.left_child
right_child: LeafNode = decision_node.right_child
left_child.set_value(sklearn_tree.value[left_child_index][0][0])
right_child.set_value(sklearn_tree.value[right_child_index][0][0])
mutation = GrowMutation(searched_node, decision_node)
mutate(bartpy_tree, mutation)
nodes[index] = decision_node
nodes[left_child_index] = decision_node.left_child
nodes[right_child_index] = decision_node.right_child
search(left_child_index)
search(right_child_index)
def setUp(self):
self.data = Data(
pd.DataFrame({
"a": [1, 2, 3],
"b": [1, 2, 3]
}).values, np.array([1, 2, 3]))
self.a = split_node(LeafNode(Split(
self.data)), (SplitCondition(0, 1, le), SplitCondition(0, 1, gt)))
self.b = self.a.left_child
self.x = self.a.right_child
self.tree = Tree([self.a, self.b, self.x])
self.c = split_node(
self.a._right_child,
(SplitCondition(1, 2, le), SplitCondition(1, 2, gt)))
mutate(self.tree, TreeMutation("grow", self.x, self.c))
self.d = self.c.left_child
self.e = self.c.right_child
def setUp(self):
X = format_covariate_matrix(
pd.DataFrame({
"a": [1, 2, 3],
"b": [1, 2, 3]
}))
self.data = Data(X, np.array([1, 2, 3]).astype(float))
self.a = split_node(LeafNode(Split(
self.data)), (SplitCondition(0, 1, le), SplitCondition(0, 1, gt)))
self.b = self.a.left_child
self.x = self.a.right_child
self.tree = Tree([self.a, self.b, self.x])
self.c = split_node(
self.a._right_child,
(SplitCondition(1, 2, le), SplitCondition(1, 2, gt)))
mutate(self.tree, TreeMutation("grow", self.x, self.c))
self.d = self.c.left_child
self.e = self.c.right_child
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 test_split(self):
left_split_condition = SplitCondition(0, 3, le)
right_split_condition = SplitCondition(0, 3, gt)
updated_node = split_node(
self.node, [left_split_condition, right_split_condition])
self.assertIsInstance(updated_node, DecisionNode)
self.assertEqual(updated_node.data.y.summed_y(), 15)
self.assertEqual(updated_node.left_child.data.y.summed_y(), 6)
self.assertEqual(updated_node.right_child.data.y.summed_y(), 9)
self.assertEqual(updated_node.data.X.n_obsv, 5)
self.assertEqual(updated_node.left_child.data.X.n_obsv, 3)
self.assertEqual(updated_node.right_child.data.X.n_obsv, 2)
updated_node.update_y([2.0, 4.0, 6.0, 8.0, 10.0])
self.assertEqual(updated_node.left_child.data.y.summed_y(), 12)