def log_tree_ratio_prune(model: Model, tree: Tree,
proposal: PruneMutation):
prob_chosen_split = log_probability_split_within_node(
GrowMutation(proposal.updated_node, proposal.existing_node))
if model.prior_name in ["poly_splits", "exponential_splits"]:
numerator = log_probability_node_not_split(model,
proposal.updated_node)
prob_left_not_split = log_probability_node_not_split(
model, proposal.existing_node.left_child)
prob_right_not_split = log_probability_node_not_split(
model, proposal.existing_node.left_child)
prob_updated_node_split = log_probability_node_split(
model, proposal.existing_node)
prob_chosen_split = log_probability_split_within_node(
GrowMutation(proposal.updated_node, proposal.existing_node))
denominator = prob_left_not_split + prob_right_not_split + prob_updated_node_split + prob_chosen_split
elif model.prior_name == "cond_unif":
K = len(tree.leaf_nodes)
# log gives negative for tree with single node, so don't suggest prune if single node
denominator = np.log(model.lam / (4 * K - 6)) + prob_chosen_split
numerator = 0
elif model.prior_name == "exp_prior":
denominator = -model.c + prob_chosen_split
numerator = 0
else:
return
return numerator - denominator
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 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 log_tree_ratio_prune(model: Model, proposal: PruneMutation):
numerator = log_probability_node_not_split(model, proposal.updated_node)
prob_left_not_split = log_probability_node_not_split(model, proposal.existing_node.left_child)
prob_right_not_split = log_probability_node_not_split(model, proposal.existing_node.left_child)
prob_updated_node_split = log_probability_node_split(model, proposal.existing_node)
prob_chosen_split = log_probability_split_within_node(GrowMutation(proposal.updated_node, proposal.existing_node))
denominator = prob_left_not_split + prob_right_not_split + prob_updated_node_split + prob_chosen_split
return numerator - denominator
def log_prune_transition_ratio(self, tree: Tree, mutation: PruneMutation):
prob_grow_node_selected = safe_negative_log(
n_splittable_leaf_nodes(tree) - 1)
prob_split = log_probability_split_within_node(
GrowMutation(mutation.updated_node, mutation.existing_node))
prob_grow_selected = prob_grow_node_selected + prob_split
prob_prune_selected = safe_negative_log(
n_prunable_decision_nodes(tree))
prob_selection_ratio = prob_grow_selected - prob_prune_selected
grow_prune_ratio = np.log(self.prob_method[0] / self.prob_method[1])
return grow_prune_ratio + prob_selection_ratio
def log_prune_transition_ratio(self, tree: Tree, mutation: PruneMutation):
if n_splittable_leaf_nodes(tree) == 1:
prob_grow_node_selected = -np.inf # Infinitely unlikely to be able to grow a null tree
else:
prob_grow_node_selected = -np.log(
n_splittable_leaf_nodes(tree) - 1)
prob_split = log_probability_split_within_node(
GrowMutation(mutation.updated_node, mutation.existing_node))
prob_grow_selected = prob_grow_node_selected + prob_split
prob_prune_selected = -np.log(n_prunable_decision_nodes(tree))
prob_selection_ratio = prob_grow_selected - prob_prune_selected
grow_prune_ratio = np.log(self.prob_method[0] / self.prob_method[1])
return grow_prune_ratio + prob_selection_ratio
def uniformly_sample_grow_mutation(tree: Tree) -> TreeMutation:
node = random_splittable_leaf_node(tree)
updated_node = sample_split_node(node)
return GrowMutation(node, updated_node)
def log_prune_transition_ratio(self, tree: Tree, mutation: PruneMutation):
prob_selection_ratio = log_probability_split_within_node(GrowMutation(mutation.updated_node, mutation.existing_node))
grow_prune_ratio = np.log(self.prob_method[0] / self.prob_method[1])
return grow_prune_ratio + prob_selection_ratio
def grow_mutations(tree: Tree) -> List[TreeMutation]:
return [GrowMutation(x, sample_split_node(x)) for x in tree.leaf_nodes]
