def build_tree(self, X, y):
"""
Args:
X: object-features matrix
y: target vector
Returns:
A `BinaryDecisionTree` fitted to the dataset.
The actual structure of the tree depends both on dataset and the parameters
passed to the `TreeBuilderCART` constructor.
"""
n_samples, n_features = X.shape
tree = BinaryDecisionTree(n_features=n_features)
if self.n_jobs > 1:
self.pool = Pool(self.n_jobs)
leaf_to_split = tree.root()
self._build_tree_recursive(tree, leaf_to_split, X, y)
self._prune_tree(tree, X, y)
if TreeBuilderCART.debug:
TreeBuilderCART.logger.debug(tree)
self.pool = None
return tree
def test_nodes_at_level(self):
tree = BinaryDecisionTree(n_features=1)
split = BinaryDecisionTreeSplit(feature_id=0, value=0.0)
tree.split_node(0, split)
tree.split_node(2, split)
print(tree)
self.assertEqual([0], tree.nodes_at_level(1))
self.assertEqual([1, 2], tree.nodes_at_level(2))
self.assertEqual([5, 6], tree.nodes_at_level(3))
self.assertEqual([], tree.nodes_at_level(4))
def test_multiple_splits(self):
tree = BinaryDecisionTree(n_features=1)
split = BinaryDecisionTreeSplit(feature_id=0, value=0.0)
for split_count in range(1, 10):
tree.split_node(tree.leaves()[0], split)
self.assertEqual(tree.num_of_leaves(), split_count + 1)
print(tree)
def build_tree(self, X, y):
"""
Builds a tree fitted to data set (X, y).
"""
n_samples, n_features = X.shape
tree = BinaryDecisionTree(n_features=n_features)
cur_level = 1
self._dataset = np.copy(X), np.copy(y)
self._data_per_node = {
0: self._dataset
}
self._build_tree_recursive(tree, cur_level)
self._prune_tree(tree, X, y)
if TreeBuilderOblivious.debug:
TreeBuilderOblivious.logger.debug('\n' + str(tree))
return tree
def test_depth(self):
tree = BinaryDecisionTree(n_features=1)
split = BinaryDecisionTreeSplit(feature_id=0, value=0.0)
for split_count in range(15):
tree.split_node(tree.leaves()[0], split)
print(tree)
self.assertEqual(1, tree.depth(0))
self.assertEqual(2, tree.depth(1))
self.assertEqual(2, tree.depth(2))
self.assertEqual(3, tree.depth(3))
self.assertEqual(3, tree.depth(4))
self.assertEqual(3, tree.depth(5))
self.assertEqual(3, tree.depth(6))
self.assertEqual(4, tree.depth(7))
self.assertEqual(4, tree.depth(8))
self.assertEqual(4, tree.depth(9))
self.assertEqual(4, tree.depth(10))
self.assertEqual(4, tree.depth(11))
self.assertEqual(4, tree.depth(12))
self.assertEqual(4, tree.depth(13))
self.assertEqual(4, tree.depth(14))
def test_one_split(self):
tree = BinaryDecisionTree(n_features=1)
split = BinaryDecisionTreeSplit(feature_id=0, value=0.0)
tree.split_node(0, split)
self.assertEqual(tree.num_of_leaves(), 2)
print(tree)
def test_empty_tree_str(self):
tree = BinaryDecisionTree(n_features=1)
self.assertEqual(tree.num_of_leaves(), 1)
print(tree)
