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 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 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 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_same_prediction(self):
from sklearn.ensemble import GradientBoostingRegressor
params = {
'n_estimators': 1,
'max_depth': 2,
'min_samples_split': 2,
'learning_rate': 0.8,
'loss': 'ls'
}
sklearn_model = GradientBoostingRegressor(**params)
sklearn_model.fit(self.data.X.values, self.data.y.values)
sklearn_tree = sklearn_model.estimators_[0][0].tree_
bartpy_tree = Tree([LeafNode(Split(self.data))])
map_sklearn_tree_into_bartpy(bartpy_tree, sklearn_tree)
sklearn_predictions = sklearn_tree.predict(
self.data.X.values.astype(np.float32))
sklearn_predictions = [
round(x, 2) for x in sklearn_predictions.reshape(-1)
]
bartpy_tree.cache_up_to_date = False
bartpy_tree_predictions = bartpy_tree.predict(self.data.X.values)
bartpy_tree_predictions = [
round(x, 2) for x in bartpy_tree_predictions
]
self.assertListEqual(sklearn_predictions, bartpy_tree_predictions)
def test_internal_prune(self):
updated_c = LeafNode(Split(self.data))
prune_mutation = TreeMutation("prune", self.c, updated_c)
mutate(self.tree, prune_mutation)
self.assertIn(updated_c, self.tree.leaf_nodes)
self.assertNotIn(self.c, self.tree.nodes)
self.assertNotIn(self.d, self.tree.nodes)
self.assertNotIn(self.e, self.tree.nodes)
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 setUp(self):
self.X = format_covariate_matrix(pd.DataFrame({"a": [1, 2, 3, 4, 5]}))
self.data = Data(format_covariate_matrix(self.X),
np.array([1.0, 2.0, 3.0, 4.0, 5.0]))
self.split = Split(self.data)
self.node = LeafNode(self.split)
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)