def evolve(self, X, y, n_iter=7, min_size_prune=1):
self.X = X
self.y = y
for i in range(n_iter):
#print("TAO iter ", i, " di ", n_iter)
for depth in reversed(range(self.classification_tree.depth + 1)):
#print("Ottimizzo depth", depth, "....")
T = self.classification_tree
nodes = ClassificationTree.get_nodes_at_depth(depth, T)
#print ([node.id for node in nodes])
for node in nodes:
self.optimize_nodes(node)
#pool = Pool(4)
#pool.map(self.optimize_nodes, nodes)
#pool.close()
#pool.join()
#for node in nodes:
#self.optimize_nodes(node)
#Rimetto apposto i punti associati ad ogni nodo
self.classification_tree.build_idxs_of_subtree(
X, range(len(X)), T.tree[0], oblique=T.oblique)
#Effettua il pruning finale per togliere dead branches e pure subtrees
#self.prune(min_size = min_size_prune)
ClassificationTree.restore_tree(self.classification_tree)
def optimize_crossover(tree_a, tree_b, tree_c, depth, X, labels, oblique):
d = random.choice(range(depth))
#for d in reversed(range(depth)):
nodes_a = ClassificationTree.get_nodes_at_depth(d, tree_a)
nodes_a = [node for node in nodes_a if not node.is_leaf]
all_nodes = ClassificationTree.get_nodes_at_depth(d, tree_a)
all_nodes.extend(ClassificationTree.get_nodes_at_depth(d, tree_b))
all_nodes.extend(ClassificationTree.get_nodes_at_depth(d, tree_c))
all_nodes = [node for node in all_nodes if not node.is_leaf]
#print (node.id for node in nodes_a)
#worst = nodes_a[np.argmax([ClassificationTree.misclassification_loss(node, X, labels, node.data_idxs, oblique) for node in nodes_a])]
for node in nodes_a:
if len(node.data_idxs) > 0:
best = find_best_branch(all_nodes, node.data_idxs, X, labels, oblique)
best.data_idxs = node.data_idxs
ClassificationTree.replace_node(node, best, tree_a)
def crossover(tree, trees, CR, X):
partners = random.sample(trees, 3)
partners = [ClassificationTree.copy_tree(partners[0]), ClassificationTree.copy_tree(partners[1]), ClassificationTree.copy_tree(partners[2])]
tree_nodes = list(tree.tree.values())
d = random.choice(range(1, depth))
#for d in reversed(range(tree.depth-1)):
nodes_at_depth = ClassificationTree.get_nodes_at_depth(d, tree)
other_nodes_at_depth = ClassificationTree.get_nodes_at_depth(d, partners[0])
#other_nodes_at_depth.extend(ClassificationTree.get_nodes_at_depth(d, partners[1]))
#other_nodes_at_depth.extend(ClassificationTree.get_nodes_at_depth(d, partners[2]))
#for node in nodes_at_depth:
node = random.choice(nodes_at_depth)
p = np.random.uniform()
if p < CR:
choice = random.choice(other_nodes_at_depth)
if choice.left_node != None and not choice.left_node.is_leaf and choice.right_node != None and not choice.right_node.is_leaf:
p2 = np.random.uniform()
if p2 < 0.5:
node.feature = choice.left_node.feature
node.threshold = choice.left_node.threshold
#ClassificationTree.replace_node(node, choice.left_node, tree)
else:
node.feature = choice.right_node.feature
node.threshold = choice.right_node.threshold
#ClassificationTree.replace_node(node, choice.right_node, tree)
else:
#ClassificationTree.replace_node(node, choice, tree)
node.feature = choice.feature
node.threshold = choice.threshold
'''
if choice.parent_id != -1:
node.feature = tree.tree[choice.parent_id].feature
node.threshold = tree.tree[choice.parent_id].threshold
'''
else:
node.feature = random.choice(range(len(X[0])))
node.threshold = 0